1.
2.
Andersson, My, et al.
(författare)
Improved interaction with collaborative robots - evaluation of event-specific haptic feedback in virtual reality
2024
Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 232, s. 1055-1064
Tidskriftsartikel (refereegranskat) abstract
Industry 5.0 adopts a human-centric approach that views humans as a natural part of introducing new technology, such as collaborative robots. However, one of the main challenges in implementing collaborative robots is safety, including the sense of safety. Trust is also a primary challenge when establishing functional collaboration. Influencing factors includes experience and expertise, and research shows that Virtual Reality has the potential to perform such training. This research aims to investigate whether using virtual reality with appropriate feedback can be an effective platform for familiarization and training. In our experiment, we utilized haptic feedback from commercial Virtual Reality controllers to simulate physical interactions with collaborative robots. The experiment involved the participation of fifteen individuals. The results showed that participants regarded haptic feedback while moving as the most appropriate representation. This research aims to identify whether Virtual Reality with suitable feedback can serve as a familiarization and training platform.
3.
Aoshima, Koji, et al.
(författare)
World modeling for autonomous wheel loaders
2024
Ingår i: Automation. - : MDPI. - 2673-4052. ; 5:3, s. 259-281
Tidskriftsartikel (refereegranskat) abstract
This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs that include a heightmap of the initial pile shape and action parameters for an automatic bucket-filling controller. Long-horizon planning of sequential loading in a dynamically changing environment is thus enabled as repeated model inference. The models, consisting of deep neural networks, were trained on data from a 3D multibody dynamics simulation of over 10,000 random loading actions in gravel piles of different shapes. The accuracy and inference time for predicting the loading performance and the resulting pile state were, on average, 95% in 1.21.2 ms and 97% in 4.54.5 ms, respectively. Long-horizon predictions were found feasible over 40 sequential loading actions.
4.
Armleder, Simon, et al.
(författare)
Tactile-Based Negotiation of Unknown Objects during Navigation in Unstructured Environments with Movable Obstacles
2024
Ingår i: Advanced Intelligent Systems. - 2640-4567. ; 6:3
Tidskriftsartikel (refereegranskat) abstract
Traditional robot navigation passively plans/replans to avoid any contact with obstacles in the scene. This limits the obtained solutions to the collision-free space and leads to failures if the path to the goal is obstructed. In contrast, humans actively modify their environment by repositioning objects if it assists locomotion. This article aims to bring robots closer to such abilities by providing a framework to detect and clear movable obstacles to continue navigation. The approach leverages a multimodal robot skin that provides both local proximity and tactile feedback regarding physical interactions with the surroundings. This multimodal contact feedback is employed to adapt the robot's behavior when interacting with object surfaces and regulating applied forces. This enables the robot to remove bulky obstacles from its path and solves otherwise infeasible navigation problems. The system's ability is demonstrated in simulation and real-world scenarios involving movable and nonmovable obstacles.
5.
6.
Bai, Yifan
(författare)
Synergistic Strategies in Multi-Robot Systems: Exploring Task Assignment and Multi-Agent Pathfinding
2024
Licentiatavhandling (övrigt vetenskapligt/konstnärligt) abstract
Robots are increasingly utilized in industry for their capability to perform repetitive,complex tasks in environments unsuitable for humans. This surge in robotic applicationshas spurred research into Multi-Robot Systems (MRS), which aim to tackle complex tasksrequiring collaboration among multiple robots, thereby boosting overall efficiency. However,MRS introduces multifaceted challenges that span various domains, including robot perception,localization, task assignment, communication, and control. This dissertation delves into theintricate aspects of task assignment and path planning within MRS.The first area of focus is on multi-robot navigation, specifically addressing the limitationsinherent in current Multi-Agent Path Finding (MAPF) models. Traditional MAPF solutionstend to oversimplify, treating robots as holonomic units on grid maps. While this approachis impractical in real-world settings where robots have distinct geometries and kinematicrestrictions, it is important to note that even in its simplified form, MAPF is categorized as anNP-hard problem. The complexity inherent in MAPF becomes even more pronounced whenextending these models to non-holonomic robots, underscoring the significant computationalchallenges involved. To address these challenges, this thesis introduces a novel MAPF solverdesigned for non-holonomic, heterogeneous robots. This solver integrates the hybrid A*algorithm, accommodating kinematic constraints, with a conflict-based search (CBS) for efficientconflict resolution. A depth-first search approach in the conflict tree is utilized to accelerate theidentification of viable solutions.The second research direction explores synergizing task assignment with path-finding inMRS. While there is substantial research in both decentralized and centralized task assignmentstrategies, integrating these with path-finding remains underexplored. This dissertation evaluatesdecoupled methods for sequentially resolving task assignment and MAPF challenges. Oneproposed method combines the Hungarian algorithm and a Traveling Salesman Problem (TSP)solver for swift, albeit suboptimal, task allocation. Subsequently, robot paths are generatedindependently, under the assumption of collision-free navigation. During actual navigation, aNonlinear Model Predictive Controller (NMPC) is deployed for dynamic collision avoidance. Analternative approach seeks optimal solutions by conceptualizing task assignment as a MultipleTraveling Salesman Problem (MTSP), solved using a simulated annealing algorithm. In tandem,CBS is iteratively applied to minimize the cumulative path costs of the robots.
7.
Bakhshi Valojerdi, Zeinab, 1986-, et al.
(författare)
Evaluation of Storage Placement in Computing Continuum for a Robotic Application : A Simulation-Based Performance Analysis
2024
Ingår i: Journal of Grid Computing. - : Springer Science+Business Media B.V.. - 1570-7873 .- 1572-9184. ; 22:2
Tidskriftsartikel (refereegranskat) abstract
This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage.
8.
Borngrund, Carl, 1992-, et al.
(författare)
Automating the Short-Loading Cycle: Survey and Integration Framework
2024
Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 14:11
Tidskriftsartikel (refereegranskat) abstract
The short-loading cycle is a construction task where a wheel loader scoops material from a nearby pile in order to move that material to the tipping body of a dump truck. The short-loading cycle is a vital task performed in high quantities and is often part of a more extensive never-ending process to move material for further refinement. This, together with the highly repetitive nature of the short-loading cycle, makes it a suitable candidate for automation. However, the short-loading cycle is a complex task where the mechanics of the wheel loader together with the interaction between the wheel loader and the environment needs to be considered. This must be achieved while maintaining some productivity goal and, concurrently, minimizing the used energy. The main objective of this work is to analyze the short-loading cycle, assess the current state of research in this field, and discuss the steps required to progress towards a minimal viable product consisting of individual automation solutions that can perform the short-loading cycle well enough to be used by early adopters. This is achieved through a comprehensive literature study and consequent analysis of the review results. From this analysis, the requirements of an MVP are defined and some gaps which are currently hindering the realization of the MVP are presented.
9.
Brockmann, Jan Thies, et al.
(författare)
The voraus-AD Dataset for Anomaly Detection in Robot Applications
2024
Ingår i: IEEE Transactions on robotics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1552-3098 .- 1941-0468. ; 40, s. 438-451
Tidskriftsartikel (refereegranskat) abstract
During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production. When collecting data to detect such cases, it is not ensured that data from all potentially occurring errors is included as unforeseeable events may happen over time. Therefore, anomaly detection (AD) delivers a practical solution, using only normal data to learn to detect unusual events. We introduce a dataset that allows training and benchmarking of anomaly detection methods for robotic applications based on machine data which will be made publicly available to the research community. As a typical robot task the dataset includes a pick-and-place application which involves movement, actions of the end effector, and interactions with the objects of the environment. Since several of the contained anomalies are not task-specific but general, evaluations on our dataset are transferable to other robotics applications as well. In addition, we present multivariate time-series flow (MVT-Flow) as a new baseline method for anomaly detection: It relies on deep-learning-based density estimation with normalizing flows, tailored to the data domain by taking its structure into account for the architecture. Our evaluation shows that MVT-Flow outperforms baselines from previous work by a large margin of 6.2% in area under receiving operator characteristic.
10.
Chellapurath, Mrudul, et al.
(författare)
USLIP dynamics emerges in underwater legged robot with foot kinematics of punting crabs
2024
Ingår i: Mechatronics (Oxford). - : Elsevier BV. - 0957-4158 .- 1873-4006. ; 99
Tidskriftsartikel (refereegranskat) abstract
This article investigates bioinspired solutions for achieving stable dynamic gaits in legged robots through leg coordination and foot trajectories. In this study, we recorded the kinematics of underwater running of the crab, Pachygrapsus marmoratus, and implemented the parameterized foot trajectories and inter-leg coordination on an underwater legged robot, SILVER 2.0. The robot's design parameters like legs’ stiffness, leg length, and body mass are based on the Underwater Spring Loaded Inverted Pendulum (USLIP), a model that describes underwater running in animals. With this implementation, we observed the spontaneous emergence of USLIP dynamics in 20% of the strides in the robot. This approach allowed SILVER 2.0 to leverage the advantages of stable dynamic gaits while optimizing the foot trajectory and inter-leg coordination, resulting in improved locomotion performances. The robot achieved a forward velocity of 0.16 m/s, twice the value obtained in previous gaits. Our study presents a promising approach for improving the locomotion performance of legged robots, enabling their effective use in various field applications, and further confirms a broad embedding of controllers generating template dynamics.
11.
Chintalapati, Bharadwaj, et al.
(författare)
Opportunities and challenges of on-board AI-based image recognition for small satellite Earth observation missions
2024
Ingår i: Advances in Space Research. - : Elsevier. - 0273-1177 .- 1879-1948.
Tidskriftsartikel (refereegranskat) abstract
The satellite industry is rapidly growing. There has been a significant increase in the number of new small satellites that are launched, which is complemented by the rapid pace of the development of image recognition algorithms. Convolutional neural networks (CNNs) in particular, have achieved state-of-the-art performance in computer vision related applications. Combining both and running an AI algorithm on-board the satellite to observe and recognize any natural disaster directly from the orbit is an important opportunity. This paper presents notable challenges that are generally involved in an Earth Observation small satellite mission and further challenges that are posed by combining it with AI-based image recognition on-board the satellite. This study discusses an approach that is feasible mainly for a fleet of small satellites.
12.
Correia, Filipa, et al.
(författare)
The effects of observing robotic ostracism on children's prosociality and basic needs
2024
Ingår i: HRI '24: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. - : Association for Computing Machinery (ACM). - 9798400703225 ; , s. 157-166
Konferensbidrag (refereegranskat) abstract
Research on robotic ostracism is still scarce and has only explored its effects on adult populations. Although the results revealed important carryover effects of robotic exclusion, there is no evidence yet that those results occur in child-robot interactions. This paper provides the first exploration of robotic ostracism with children. We conducted a study using the Robotic Cyberball Paradigm in a third-person perspective with a sample of 52 children aged between five to ten years old. The experimental study had two conditions: Exclusion and Inclusion. In the Exclusion condition, children observed a peer being excluded by two robots; while in the Inclusion condition, the observed peer interacted equally with the robots. Notably, even 5-year-old children could discern when robots excluded another child. Children who observed exclusion reported lower levels of belonging and control, and exhibited higher prosocial behaviour than those witnessing inclusion. However, no differences were found in children's meaningful existence, self-esteem, and physical proximity across conditions. Our user study provides important methodological considerations for applying the Robotic Cyberball Paradigm with children. The results extend previous literature on both robotic ostracism with adults and interpersonal ostracism with children. We finish discussing the broader implications of children observing ostracism in human-robot interactions.
13.
Cortinhal, Tiago, 1990-, et al.
(författare)
Depth- and semantics-aware multi-modal domain translation : Generating 3D panoramic color images from LiDAR point clouds
2024
Ingår i: Robotics and Autonomous Systems. - Amsterdam : Elsevier. - 0921-8890 .- 1872-793X. ; 171, s. 1-9
Tidskriftsartikel (refereegranskat) abstract
This work presents a new depth-and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation in a multi-modal setup between LiDAR and camera sensors. The proposed model leverages scene semantics as a mid-level representation and is able to translate raw LiDAR point clouds to RGB-D camera images by solely relying on semantic scene segments. We claim that this is the first framework of its kind and it has practical applications in autonomous vehicles such as providing a fail-safe mechanism and augmenting available data in the target image domain. The proposed model is evaluated on the large-scale and challenging Semantic-KITTI dataset, and experimental findings show that it considerably outperforms the original TITAN-Net and other strong baselines by 23.7% margin in terms of IoU. © 2023 The Author(s).
14.
Coser, Omar, et al.
(författare)
AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb : a review
2024
Ingår i: Frontiers in Robotics and AI. - : Frontiers Media S.A.. - 2296-9144. ; 11
Forskningsöversikt (refereegranskat) abstract
Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.
15.
Cosier, Lucas, et al.
(författare)
A Unifying Variational Framework for Gaussian Process Motion Planning
2024
Ingår i: Proceedings of Machine Learning Research. - 2640-3498.
Konferensbidrag (refereegranskat) abstract
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions. A motion planning algorithm must therefore balance competing demands, and should ideally incorporate uncertainty to handle noise, model errors, and facilitate deployment in complex environments. To address these issues, we introduce a framework for robot motion planning based on variational Gaussian Processes, which unifies and generalizes various probabilistic- inference-based motion planning algorithms. Our framework provides a principled and flexible way to incorporate equality-based, inequality-based, and soft motion- planning constraints during end-to-end training, is straightforward to implement, and provides both interval-based and Monte-Carlo-based uncertainty estimates. We conduct experiments using different environments and robots, comparing against baseline approaches based on the feasibility of the planned paths, and obstacle avoidance quality. Results show that our proposed approach yields a good balance between success rates and path quality.
16.
Costen, Clarissa, et al.
(författare)
Multi-Robot Allocation of Assistance from a Shared Uncertain Operator
2024
Ingår i: AAMAS 2024 - Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. - : International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). ; , s. 400-408
Konferensbidrag (refereegranskat) abstract
Shared autonomy systems allow robots to either operate autonomously or request assistance from a human operator. In such settings, the human operator may exhibit sub-optimal behaviours, influenced by latent variables such as attention level or task proficiency. In this paper, we consider shared autonomy systems composed of multiple robots and one human. In this setting, we aim to synthesise a controller that selects, at each decision step, the actions to be taken by each robot and which (if any) robot the human operator should assist. To efficiently allocate the human operator to a robot at any given time, we propose a controller that reasons about the uncertainty over the latent variables impacting the human operator's performance. To ensure scalability, we use an online bidding system, where each robot plans while considering its belief over the human's performance, and bids according to the direct benefit of human assistance and how much information will be gained by the system about the human. We experiment on two domains, where we outperform approaches for allocation of human assistance that do not consider the human's latent variables, and show that the performance of the overall system increases when robots consider the information gained by requesting human assistance when bidding.
17.
Cronrath, Constantin, 1990, et al.
(författare)
How Useful is Learning in Mitigating Mismatch Between Digital Twins and Physical Systems?
2024
Ingår i: IEEE Transactions on Automation Science and Engineering. - 1558-3783 .- 1545-5955. ; 21:1, s. 758-770
Tidskriftsartikel (refereegranskat) abstract
In the control of complex systems, we observe two diametrical trends: model-based control derived from digital twins, and model-free control through AI. There are also attempts to bridge the gap between the two by incorporating learning-based AI algorithms into digital twins to mitigate mismatches between the digital twin model and the physical system. One of the most straightforward approaches to this is direct input adaptation. In this paper, we ask whether it is useful to employ a generic learning algorithm in such a setting, and our conclusion is "not very". We denote an algorithm to be more useful than another algorithm based on three aspects: 1) it requires fewer data samples to reach a desired minimal performance, 2) it achieves better performance for a reasonable number of data samples, and 3) it accumulates less regret. In our evaluation, we randomly sample problems from an industrially relevant geometry assurance context and measure the aforementioned performance indicators of 16 different algorithms. Our conclusion is that blackbox optimization algorithms, designed to leverage specific properties of the problem, generally perform better than generic learning algorithms, once again finding that "there is no free lunch".
18.
Cui, Shaohua, et al.
(författare)
Temporal Finite-Time Adaptation in Controlling Quantized Nonlinear Systems Amidst Time-Varying Output Constraints
2024
Ingår i: IEEE Transactions on Automation Science and Engineering. - 1558-3783 .- 1545-5955. ; In Press
Tidskriftsartikel (refereegranskat) abstract
Using the backstepping technique, this paper formulates innovative adaptive finite-time stabilizing controllers for uncertain nonlinear systems featuring nonuniform input quantization and asymmetric, time-varying output constraints. These novel controllers leverage the consistent characteristics of both hysteresis quantizers and logarithmic quantizers. Quantization errors, when consistent, become unbounded and contingent on control input, rendering them incompatible with the growth conditions of nonlinear systems. Consequently, the developed adaptive controllers eliminate the reliance on growth conditions, effectively addressing the impact of unbounded quantization errors on finite-time stability. This adaptability allows the controllers to function effectively with systems employing either hysteresis quantizers or logarithmic quantizers. The paper establishes the convergence of these controllers through the finite-time Lyapunov stability theorem. It also provides a comprehensive guideline for tuning settling time, enabling fine-grained control over finite-time convergence and adjustable tracking error performance. Additionally, the controllers rigorously maintain system output within predefined limits. Their effectiveness and low computational burden are demonstrated through three comparative numerical simulations and a practical simulation in collision-free trajectory tracking control of an autonomous vehicle platoon using the vehicle motion software CarSim. These simulations confirm the advanced performance of the adaptive controllers. Note to Practitioners—This paper introduces an innovative approach to control uncertain nonlinear systems encountering intricate input quantization and output constraints. Employing the sophisticated backstepping technique, the authors present adaptive finite-time-stabilizing controllers engineered to address nonuniform input quantization and asymmetric, time-varying output restrictions. What distinguishes these controllers is their reliance on the consistent behavior exhibited by hysteresis and logarithmic quantizers. This unique feature equips them to effectively counteract unbounded quantization errors influenced by control input. Most notably, these controllers eliminate the conventional growth conditions typically demanded by nonlinear systems. As a result, they extend their applicability to a broad spectrum of systems employing either hysteresis or logarithmic quantizers. The research also provides practitioners with a valuable guideline for precisely adjusting settling time. This enables the attainment of desired convergence rates while permitting adaptable tracking error performance. Additionally, these controllers guarantee that the system’s output adheres to predefined limits. The practical significance of this study is highlighted through three comparative numerical simulations and a real-world application simulation. This real-world simulation involves collision-free trajectory tracking control of an autonomous vehicle platoon, executed using the vehicle motion software CarSim. These simulations unequivocally demonstrate the effectiveness and low computational burden of the developed controllers, thereby establishing them as a valuable resource for practitioners facing complex control challenges in various domains.
19.
Cumbal, Ronald
(författare)
Robots Beyond Borders : The Role of Social Robots in Spoken Second Language Practice
2024
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
This thesis investigates how social robots can support adult second language (L2) learners in improving conversational skills. It recognizes the challenges inherent in adult L2 learning, including increased cognitive demands and the unique motivations driving adult education. While social robots hold potential for natural interactions and language education, research into conversational skill practice with adult learners remains underexplored. Thus, the thesis contributes to understanding these conversational dynamics, enhancing speaking practice, and examining cultural perspectives in this context.To begin, this thesis investigates robot-led conversations with L2 learners, examining how learners respond to moments of uncertainty. The research reveals that when faced with uncertainty, learners frequently seek clarification, yet many remain unresponsive. As a result, effective strategies are required from robot conversational partners to address this challenge. These interactions are then used to evaluate the performance of off-the-shelf Automatic Speech Recognition (ASR) systems. The assessment highlights that speech recognition for L2 speakers is not as effective as for L1 speakers, with performance deteriorating for both groups during social conversations. Addressing these challenges is imperative for the successful integration of robots in conversational practice with L2 learners.The thesis then explores the potential advantages of employing social robots in collaborative learning environments with multi-party interactions. It delves into strategies for improving speaking practice, including the use of non-verbal behaviors to encourage learners to speak. For instance, a robot's adaptive gazing behavior is used to effectively balance speaking contributions between L1 and L2 pairs of participants. Moreover, an adaptive use of encouraging backchannels significantly increases the speaking time of L2 learners.Finally, the thesis highlights the importance of further research on cultural aspects in human-robot interactions. One study reveals distinct responses among various socio-cultural groups in interaction between L1 and L2 participants. For example, factors such as gender, age, extroversion, and familiarity with robots influence conversational engagement of L2 speakers. Additionally, another study investigates preconceptions related to the appearance and accents of nationality-encoded (virtual and physical) social robots. The results indicate that initial perceptions may lead to negative preconceptions, but that these perceptions diminish after actual interactions.Despite technical limitations, social robots provide distinct benefits in supporting educational endeavors. This thesis emphasizes the potential of social robots as effective facilitators of spoken language practice for adult learners, advocating for continued exploration at the intersection of language education, human-robot interaction, and technology.
20.
Cumbal, Ronald, et al.
(författare)
Speaking Transparently : Social Robots in Educational Settings
2024
Ingår i: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI '24 Companion), March 11--14, 2024, Boulder, CO, USA.
Konferensbidrag (refereegranskat) abstract
The recent surge in popularity of Large Language Models, known for their inherent opacity, has increased the interest in fostering transparency in technology designed for human interaction. This concern is equally prevalent in the development of Social Robots, particularly when these are designed to engage in critical areas of our society, such as education or healthcare. In this paper we propose an experiment to investigate how users can be made aware of the automated decision processes when interacting in a discussion with a social robot. Our main objective is to assess the effectiveness of verbal expressions in fostering transparency within groups of individuals as they engage with a robot. We describe the proposed interactive settings, system design, and our approach to enhance the transparency in a robot's decision-making process for multi-party interactions.
21.
Dalklint, Anna, et al.
(författare)
Simultaneous shape and topology optimization of inflatable soft robots
2024
Ingår i: Computer Methods in Applied Mechanics and Engineering. - 0045-7825. ; 420
Tidskriftsartikel (refereegranskat) abstract
Simultaneous shape and topology optimization is used to design pressure-activated inflatable soft robots. The pressure loaded boundary is meshed conformingly and shape optimized, while the morphology of the robot is topology optimized. The design objective is to exert maximum force on an object, i.e. to produce soft “grippers”. The robot's motion is modeled using nearly incompressible finite deformation hyperelasticity. To ensure stability of the robot, the buckling load factors obtained via linearized buckling analyses are constrained. The finite element method is used to evaluate the optimization cost and constraint functions and the adjoint method is employed to compute their sensitivities. The numerical examples produce pressure-driven soft robots with varying complexity. We also compare our simultaneous optimization results to those obtained via sequential topology and then shape optimization.
22.
Damigos, Gerasimos, et al.
(författare)
Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging
2024
Ingår i: Journal of Intelligent and Robotic Systems. - : Springer. - 0921-0296 .- 1573-0409. ; 110:1
Tidskriftsartikel (refereegranskat) abstract
Multiple modern robotic applications benefit from centralized cognition and processing schemes. However, modern equipped robotic platforms can output a large amount of data, which may exceed the capabilities of modern wireless communication systems if all data is transmitted without further consideration. This research presents a multi-agent, centralized, and real-time 3D point cloud map merging scheme for ceaselessly connected robotic agents. Centralized architectures enable mission awareness to all agents at all times, making tasks such as search and rescue more effective. The centralized component is placed on an edge server, ensuring low communication latency, while all agents access the server utilizing a fifth-generation (5G) network. In addition, the proposed solution introduces a communication-aware control function that regulates the transmissions of map instances to prevent the creation of significant data congestion and communication latencies as well as address conditions where the robotic agents traverse in limited to no coverage areas. The presented framework is agnostic of the used localization and mapping procedure, while it utilizes the full power of an edge server. Finally, the efficiency of the novel established framework is being experimentally validated based on multiple scenarios.
23.
Damigos, Gerasimos
(författare)
Towards 5G-Enabled Intelligent Machines
2024
Licentiatavhandling (övrigt vetenskapligt/konstnärligt) abstract
This thesis introduces a novel framework for enabling intelligent machines and robots with the fifth-generation (5G) cellular network technology. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), Autonomous Guided Vehicles (AGVs), and more, can notably benefit from multi-agent collaboration, human supervision, or operation guidance, as well as from external computational units such as cloud edge servers, in all of which a framework to utilize reliable communication infrastructure is needed. Autonomous robots are often employed to alleviate humans by operating demanding missions such as inspection and data collection in harsh environments or time-critical operations in industrial environments - to name a few. For delivering data to other robots to maximize the effectiveness of the considered mission, for executing complex algorithms by offloading them into the edge cloud, or for including a human operator/supervisor into the loop, the 5G network and its advanced Quality of Service (QoS) features can be employed to facilitate the establishment of such a framework. This work focuses on establishing a baseline for integrating various time-critical robotics platforms and applications with a 5G network. These applications include offloading computationally intensive Model Predictive Control (MPC) algorithms for trajectory tracking of UAVs into the edge cloud, adapting data sharing in multi-robot systems based on network conditions, and enhancing network-aware surrounding autonomy components. We have identified a set of key performance indicators (KPIs) crucially affecting the performance of network-dependent robots and applications. We have proposed novel solutions and mechanisms to meet these requirements, which aim to combine traditional robotics techniques to enhance mission reliability with the exploitation of 5G features such as the QoS framework. Ultimately, our goal was to develop solutions that adhere to the essential paradigm of co-designing robotics with networks. We thoroughly evaluated all presented research using real-life platforms and 5G networks.
24.
Damschen, Marvin, et al.
(författare)
WayWise: A rapid prototyping library for connected, autonomous vehicles
2024
Ingår i: Software Impacts. - 2665-9638. ; , s. 100682-100682
Tidskriftsartikel (refereegranskat) abstract
WayWise is an innovative C++ and Qt-based rapid prototyping library designed to advance the development and analysis of connected, autonomous vehicles (CAVs) and Unmanned Arial Systems (UASs). It was deployed on model-sized cars and trucks as well as full-sized mobile machinery, tractors and UASs. It is actively being used in several European research projects. Developed by the RISE Dependable Transport Systems unit, the library facilitates exploration into safety and cybersecurity aspects inherent to various emerging vehicular applications within road traffic and offroad applications. This non-production library emphasizes rapid prototyping, leveraging commercial off-the-shelf hardware and the different protocols for vehicle-control communication, mainly focusing on MAVLINK. The utility of WayWise in rapidly evaluating complex vehicular behaviors is demonstrated through various research projects, thus contributing to the field of autonomous vehicular technology.
25.
Das, Shemonto, et al.
(författare)
Active learning strategies for robotic tactile texture recognition tasks
2024
Ingår i: Frontiers in Robotics and AI. - : Frontiers Media S.A.. - 2296-9144. ; 11
Tidskriftsartikel (refereegranskat) abstract
Accurate texture classification empowers robots to improve their perception and comprehension of the environment, enabling informed decision-making and appropriate responses to diverse materials and surfaces. Still, there are challenges for texture classification regarding the vast amount of time series data generated from robots’ sensors. For instance, robots are anticipated to leverage human feedback during interactions with the environment, particularly in cases of misclassification or uncertainty. With the diversity of objects and textures in daily activities, Active Learning (AL) can be employed to minimize the number of samples the robot needs to request from humans, streamlining the learning process. In the present work, we use AL to select the most informative samples for annotation, thus reducing the human labeling effort required to achieve high performance for classifying textures. We also use a sliding window strategy for extracting features from the sensor’s time series used in our experiments. Our multi-class dataset (e.g., 12 textures) challenges traditional AL strategies since standard techniques cannot control the number of instances per class selected to be labeled. Therefore, we propose a novel class-balancing instance selection algorithm that we integrate with standard AL strategies. Moreover, we evaluate the effect of sliding windows of two-time intervals (3 and 6 s) on our AL Strategies. Finally, we analyze in our experiments the performance of AL strategies, with and without the balancing algorithm, regarding f1-score, and positive effects are observed in terms of performance when using our proposed data pipeline. Our results show that the training data can be reduced to 70% using an AL strategy regardless of the machine learning model and reach, and in many cases, surpass a baseline performance. Finally, exploring the textures with a 6-s window achieves the best performance, and using either Extra Trees produces an average f1-score of 90.21% in the texture classification data set.
26.
Das, Sandipan
(författare)
State estimation with auto-calibrated sensor setup
2024
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
Localization and mapping is one of the key aspects of driving autonomously in unstructured environments. Often such vehicles are equipped with multiple sensor modalities to create a 360o sensing coverage and add redundancy to handle sensor dropout scenarios. As the vehicles operate in underground mining and dense urban environments the Global navigation satellite system (GNSS) is often unreliable. Hence, to create a robust localization system different sensor modalities like camera, lidar and IMU are used along with a GNSS solution. The system must handle sensor dropouts and work in real-time (~15 Hz), so that there is enough computation budget left for other tasks like planning and control. Additionally, precise localization is also needed to map the environment, which may be later used for re-localization of the autonomous vehicles as well. Finally, for all of these to work seamlessly, accurate calibration of the sensors is of utmost importance.In this PhD thesis, first, a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data is presented. State estimation was performed in real-time, which produced robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS components using a factor graph framework. The proposed method handled signal loss with a novel synchronization and fusion mechanism. To validate the approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ~ 7 Km). An average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair is reported. Since precise calibration is needed for the localization and mapping tasks, in this thesis, methods for real-time calibration of the sensor setup is proposed. First, a method is proposed to calibrate sensors with non-overlapping field-of-view. The calibration quality is verified by mapping known features in the environment. Nevertheless, the verification process was not real-time and no observability analysis was performed which could give us an indicator of the analytical traceability of the trajectory required for motion-based online calibration. Hence, a new method is proposed where calibration and verification were performed in real-time by matching estimated sensor poses in real-time with observability analysis. Both of these methods relied on estimating the sensor poses using the state estimator developed in our earlier works. However, state estimators have inherent drifts and they are computationally intensive as well. Thus, another novel method is developed where the sensors could be calibrated in real-time without the need for any state estimation.
27.
Dey, Samiran, et al.
(författare)
BliMSR : Blind Degradation Modelling for Generating High-Resolution Medical Images
2024
Ingår i: MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, MIUA 2023. - : Springer Nature. - 9783031485923 - 9783031485930 ; , s. 64-78
Konferensbidrag (refereegranskat) abstract
A persisting problem with existing super-resolution (SR) models is that they cannot produce minute details of anatomical structures, pathologies, and textures critical for proper diagnosis. This is mainly because they assume specific degradations like bicubic downsampling or Gaussian noise, whereas, in practice, the degradations can be more complex and hence need to be modelled "blindly". We propose a novel attention-based GAN model for medical image super-resolution that models the degradation in a data-driven agnostic way ("blind") to achieve better fidelity of diagnostic features in medical images. We introduce a new ensemble loss in the generator that boosts performance and a spectral normalisation in the discriminator to enhance stability. Experimental results on lung CT scans demonstrate that our model, BliMSR, produces super-resolved images with enhanced details and textures and outperforms recent competing models, including a diffusion model for generating super-resolution images, thus establishing a state-of-the-art. The code is available at https://github.com/Samiran-Dey/BliMSR.
28.
Diehl, Maximilian, 1995, et al.
(författare)
Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation in Robotic Tasks*
2024
Ingår i: IEEE Robotics and Automation Letters. - 2377-3766. ; 9:2, s. 1011 -1018
Tidskriftsartikel (refereegranskat) abstract
Robots acting in human environments will often face new situations and can benefit from transferring prior experience. Priors could enable robots to handle new tasks zero-shot and help prevent failures, which can be particularly costly in real robot applications. Due to their interpretable nature, causal Bayesian Networks (CBN) are popular for modeling cause-effect relations between semantically meaningful environment features and their effects on action success. While the CBN structure is often intuitively transferable to a new context, its probability distribution might change, requiring data-intensive relearning. In this work, we propose three strategies that utilize semantic similarity and relatedness between the variables of two CBNs to generate and transfer informed CBN distribution priors. We evaluate the parameter prior accuracy in five different transfer scenarios, including sim-2-real, transferring parameters to more complex tasks with a larger number of parameters and even between two different tasks, which is particularly challenging. We show that the priors lead to better distribution estimates, particularly under a limited amount of new experiments, and improve the robot's ability to predict and prevent action failures by up to 50%.
29.
Divinyi, Andreas, et al.
(författare)
On-Chip Sensors for Temperature Monitoring of Packaged GaN MMICs
2024
Ingår i: IEEE Transactions on Components, Packaging and Manufacturing Technology. - 2156-3985 .- 2156-3950. ; 14:5, s. 891-896
Tidskriftsartikel (refereegranskat) abstract
A novel approach to on-chip temperature sensors for non-invasive thermal characterization and monitoring of packaged GaN MMICs is presented. The proposed sensor is fully compatible with commercial GaN foundry processes and enables improved reliability estimation of highly integrated systems. A dedicated test structure is developed to demonstrate the capabilities of the sensor, and an accurate calibration method of its temperature response is proposed. This combination allows for continuous temperature monitoring during operation with electrical acquisition of temperature transients. The method also enables the thermal characterization of the device and package.
30.
Dust, Lukas, et al.
(författare)
A Model-Based Methodology for Automated Verification of ROS 2 Systems
2024
Konferensbidrag (refereegranskat) abstract
To simplify the formal verification of ROS 2-based applications, in this paper, we propose a novel approach to the automation of their model-based verification using model-driven engineering techniques. We propose a methodology starting with ROS 2 execution traces, generated by ROS2_tracing and using models and model transformations in Eclipse to automatically initialize pre-defined formal model templates in UPPAAL, with system parameters. While the methodology targets the simplification of formal verification for robotics developers as users, the implementation is at an early stage and the toolchain is not fully implemented and evaluated. Hence, this paper targets tool developers and researchers to give a first overview of the underlying idea of automating ROS 2 verification.Hence, we propose a toolchain that supports verification of implemented and conceptual ROS 2 systems, as well as iterative verification of timing and scheduling parameters. We propose using four different model representations, based on the ROS2_tracing output and self-designed Eclipse Ecore metamodels to model the system from a structural and verification perspective. The different model representations allow traceability throughout the modeling and verification process.Last, an initial proof of concept is implemented containing the core elements of the proposed toolchain and validated given a small ROS 2 system.
31.
Dust, Lukas
(författare)
Verifying ROS 2 Based Distributed Robotic Systems
2024
Licentiatavhandling (övrigt vetenskapligt/konstnärligt) abstract
Due to safety criticality, distributed robotic systems, such as Robot Operating System 2 (ROS 2) based applications, often have strict timing requirements. In this thesis, we attempt to simplify formal verification of the timing behaviour of ROS 2 based applications. Therefore, (i) we conduct experiments to determine and define patterns and semantics of ROS 2 task scheduling and execution, (ii) we propose a pattern-based formal approach of modeling and verifying ROS 2 applications via model checking in UPPAAL, and (iii) we propose a methodology for model-based development and verification of ROS 2 application designs. The thesis starts with a comprehensive evaluation of timing behavior, including the internal scheduling of ROS 2 applications, to define evaluation metrics and timing correctness. Based on the evaluation, buffer overflow and callback latency are defined as measures for timing errors. Furthermore, we identify application design patterns and parameters that can influence potential timing errors. To introduce and facilitate the use of formal methods, we propose pattern-based verification, using UPPAAL, creating reusable templates of important ROS 2 application components. Furthermore, we show how to apply the templates to model ROS 2 applications and verify potential buffer overflow and callback latencies. Finally, we propose a novel methodology for automation of verification in the context of ROS 2 that uses generated tracing information of ROS 2 executions to build structural models as class diagrams and, ultimately, formal models in the form of networks of UPPAAL timed automata for model checking. In our approach, one can apply the methodology as a framework that includes model checking as a back-end and, therefore, helping designers to bridge the gap between the ROS 2 code and formal analysis.
32.
Edstedt, Johan, et al.
(författare)
DeDoDe: Detect, Don't Describe - Describe, Don't Detect for Local Feature Matching
2024
Ingår i: Proceedings - 2024 International Conference on 3D Vision, 3DV 2024. ; , s. 148-157
Konferensbidrag (refereegranskat) abstract
Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Crucially, the detected points need to be consistent between views, i.e., correspond to the same 3D point in the scene. One of the main challenges with keypoint detection is the formulation of the learning objective. Previous learning-based methods typically jointly learn descriptors with keypoints, and treat the keypoint detection as a binary classification task on mutual nearest neighbours. However, basing keypoint detection on descriptor nearest neighbours is a proxy task, which is not guaranteed to produce 3D-consistent keypoints. Furthermore, this ties the keypoints to a specific descriptor, complicating downstream usage. In this work, we instead learn keypoints directly from 3D consistency. To this end, we train the detector to detect tracks from large-scale SfM. As these points are often overly sparse, we derive a semi-supervised two-view detection objective to expand this set to a desired number of detections. To train a descriptor, we maximize the mutual nearest neighbour objective over the keypoints with a separate network. Results show that our approach, DeDoDe, achieves significant gains on multiple geometry benchmarks. Code is provided at http://github.com/Parskatt/DeDoDegithub.com/Parskatt/DeDoDe.
33.
Erös, Endre, 1990
(författare)
On intelligent automation systems
2024
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
Developing automation systems that are capable of handling dynamic and unpredictable situations is a challenging task, as it requires adapting to a changing environment and managing potentially unforeseen action outcomes. In contrast to traditional automation, where control code is explicitly pro- grammed, a model-based approach might be a more appropriate solution for automating such systems. Such an approach allows for integrating planning algorithms, which can enable the generation of control sequences that consider the system’s state. This capability is essential in enabling human-robot col- laboration and handling error recovery and restart. We refer to such a model- based and goal-oriented approach to automation as Intelligent Automation Systems (IAS). To bridge the gap between research and practical utilization, this thesis aims to facilitate the development of IAS by investigating methods for their preparation, control, and testing. A framework for preparation and virtual commissioning of IAS is presented, which compiles the necessary methods into a high-level structure, aiming to streamline the IAS development process. As part of the preparation process, an effort to explain the unsolvability of some planning problems by localiz- ing potential faults in behavior models is presented. Furthermore, this thesis investigates planning and SAT solving methods aimed at improving the effi- ciency of planning, thereby enhancing the responsiveness and adaptability of IAS. A planning and execution framework for IAS is presented, with a focus on handling dynamic and unpredictable systems. Finally, an iterative method for the verification of IAS is presented, where methods such as supervisory con- trol theory, model checking, unit and integration testing, and property-based testing play key roles in ensuring the correct behavior of IAS. Connected to verification, a criterion for assessing the test coverage of IAS is presented. This research contributes to the field of intelligent automation by providing solutions for the development, control, and verification of systems designed for complex and unpredictable environments, aiming to bridge the gap between theory and practice.
34.
Fadakar, Alireza, et al.
(författare)
Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning
2024
Ingår i: IEEE Open Journal of the Communications Society. - 2644-125X. ; 5, s. 3299-3314
Tidskriftsartikel (refereegranskat) abstract
Reconfigurable intelligent surfaces (RISs) have received considerable attention in applications related to localization. However, operation in multi-path scenarios is challenging from both complexity and performance perspectives. This study presents a two-stage low complexity method for joint three-dimensional (3D) localization and synchronization using multiple RISs. Firstly, the received signals are preprocessed, and an efficient deep learning architecture is proposed to initially estimate the angles of departure (AODs) of the virtual line of sight paths from the RISs to the user. Then, a hybrid asynchronous AOD time-of-arrival-based approach is proposed in the first stage to estimate an initial guess of the position of the user equipment (UE). Finally, in the second stage, an optimization problem is formulated to refine the position of the UE by effectively utilizing the estimated delays and the clock offset. Our comparative study reveals that the proposed method outperforms the existing methods in terms of accuracy and complexity. Notably, the proposed method showcases enhanced robustness against multipath effects when compared to the state-of-the-art approaches.
35.
Farisco, Michele, et al.
(författare)
A method for the ethical analysis of brain-inspired AI
2024
Ingår i: Artificial Intelligence Review. - : Springer. - 0269-2821 .- 1573-7462. ; 57:6
Tidskriftsartikel (refereegranskat) abstract
Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limitations are arguably both conceptual (e.g., related to the underlying theoretical models, such as symbolic vs.connectionist), and operational (e.g., related to robustness and ability to generalize). Biologically inspired AI, and more specifically brain-inspired AI, promises to provide further biological aspects beyond those that are already traditionally included in AI, making it possible to assess and possibly overcome some of its present shortcomings. This article examines some conceptual, technical, and ethical issues raised by the development and use of brain-inspired AI. Against this background, the paper asks whether there is anything ethically unique about brain-inspired AI. The aim of the paper is to introduce a method that has a heuristic nature and that can be applied to identify and address the ethical issues arising from brain-inspired AI (and from AI more generally). The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.
36.
Farisco, Michele
(författare)
The ethical implications of indicators of consciousness in artificial systems
2024
Ingår i: Developments in Neuroethics and Bioethics. - 2589-2959.
Tidskriftsartikel (refereegranskat) abstract
The prospect of artificial consciousness raises theoretical, technical and ethical challenges which converge on the core issue of how to eventually identify and characterize it. In order to provide an answer to this question, I propose to start from a theoretical reflection about the meaning and main characteristics of consciousness. On the basis of this conceptual clarification it is then possible to think about relevant empirical indicators (i.e. features that facilitate the attribution of consciousness to the system considered) and identify key ethical implications that arise. In this chapter, I further elaborate previous work on the topic, presenting a list of candidate indicators of consciousness in artificial systems and introducing an ethical reflection about their potential implications. Specifically, I focus on two main ethical issues: the conditions for considering an artificial system as a moral subject; and the need for a non-anthropocentric approach in reflecting about the science and the ethics of artificial consciousness.
37.
Farooqui, Ashfaq, et al.
(författare)
On Active Learning for Supervisor Synthesis
2024
Ingår i: IEEE Transactions on Automation Science and Engineering. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-5955 .- 1558-3783. ; 21, s. 78-
Tidskriftsartikel (refereegranskat) abstract
Supervisory control theory provides an approach to synthesize supervisors for cyber-physical systems using a model of the uncontrolled plant and its specifications. These supervisors can help guarantee the correctness of the closed-loop controlled system. However, access to plant models is a bottleneck for many industries, as manually developing these models is an error-prone and time-consuming process. An approach to obtaining a supervisor in the absence of plant models would help industrial adoption of supervisory control techniques. This paper presents, an algorithm to learn a controllable supervisor in the absence of plant models. It does so by actively interacting with a simulation of the plant by means of queries. If the obtained supervisor is blocking, existing synthesis techniques are employed to prune the blocking supervisor and obtain the controllable and non-blocking supervisor. Additionally, this paper presents an approach to interface the with a PLC to learn supervisors in a virtual commissioning setting. This approach is demonstrated by learning a supervisor of the well-known example simulated in Xcelgo Experior and controlled using a PLC. interacts with the PLC and learns a controllable supervisor for the simulated system. Note to Practitioners—Ensuring the correctness of automated systems is crucial. Supervisory control theory proposes techniques to help build control solutions that have certain correctness guarantees. These techniques rely on a model of the system. However, such models are typically unavailable and hard to create. Active learning is a promising technique to learn models by interacting with the system to be learned. This paper aims to integrate active learning and supervisory control such that the manual step of creating models is no longer needed, thus, allowing the use of supervisory control techniques in the absence of models. The proposed approach is implemented in a tool and demonstrated using a case study.
38.
Fälldin, Arvid, et al.
(författare)
Open data, models, and software for machine automation
2024
Ingår i: IUFRO 2024.
Konferensbidrag (övrigt vetenskapligt/konstnärligt) abstract
We create partially annotated datasets from field measurements for developing models and algorithms for perception and control of forest machines using artificial intelligence, simulation, and experiments on physical testbeds. The datasets, algorithms, and trained models for object identification, 3D perception, and motion planning and control will be made publicly available through data and code-sharing repositories.The data is recorded using forest machines and other equipment with suitable sensors operating in the forest environment. The data include the machine and crane tip position at high resolution, and event time logs (StanForD) while the vehicle operates in high-resolution laser-scanned forest areas. For annotation, the plan is to use both CAN-bus data and audiovisual data from operators that are willing to participate in the research. Also, by fusing visual perception with operator tree characteristics input or decision, we aim to develop a method for auto-annotation, facilitating a rapid increase in labeled training data for computer vision. In other activities, images of tree plants and bark are collected.Research questions include, how to automate the process of creating annotated datasets and train models for identifying and positioning forestry objects, such as plants, tree species, logs, terrain obstacles, and do 3D reconstruction for motion planning and control? How large and varied datasets are required for the models to handle the variability in forests, weather, light conditions, etc.? Would additional synthetic data increase model inference accuracy?In part we focus on forwarders traversing terrain, avoiding obstacles, and loading or unloading logs, with consideration for efficiency, safety, and environmental impact. We explore how to auto-generate and calibrate forestry machine simulators and automation scenario descriptions using the data recorded in the field. The demonstrated automation solutions serve as proofs-of-concept and references, important for developing commercial prototypes and for understanding what future research should focus on.
39.
Ge, Yu, 1995
(författare)
Single Base Station mmWave Radio Positioning, Mapping, and SLAM
2024
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
Fifth-generation (5G) communication systems in Frequency Range 2, operating above 24 GHz and utilizing mmWave signals, showcase distinct properties that open up new possibilities in positioning, mapping, and simultaneous localization and mapping (SLAM). The combination of large bandwidth, extensive antenna arrays, and high carrier frequency results in geometric-based signals, and unprecedented delay and angle resolution. These enable the system to resolve multipath components, providing high-accuracy geometric information among the user equipment (UE), the base station (BS), and the environment. These high-accuracy geometric information allows for highly accurate UE positioning, environment mapping, and SLAM, all achievable using a single BS. While numerous studies have delved into the single BS positioning and mapping problem using snapshot measurements, a significant portion of them remains confined to theoretical analyses with many simplified assumptions. Real-world experimental validation is scarce, particularly in scenarios involving a commercial 5G BS. Additionally, while diffuse multipath contains valuable geometric information, it is often treated as a perturbation or fails to acknowledge that diffuse multipath signals may originate from the same source landmark, leading to information loss. When extending positioning and mapping to a SLAM problem by tracking the UE over time, a radio SLAM problem emerges, posing the primary challenge of effectively addressing the data association (DA) problem. It is these research gaps and challenges that drive the motivation behind this thesis. Within this thesis, [Paper A] and [Paper B] address the radio SLAM problem, with [Paper A] additionally exploring the utilization of diffuse multipath. In [Paper A], we adopt an end-to-end approach to address the radio SLAM problem, presenting a comprehensive framework for SLAM. This includes the introduction of a random finite set (RFS)-based SLAM filter designed to overcome the DA challenge inherent in radio SLAM, along with a method to effectively leverage all paths originating from the same landmark. Meanwhile, an efficient alternative RFS-based SLAM filter, designed for real-time implementation, is proposed in [Paper B] as a counterpart to the solution presented in [Paper A]. In [Paper C], we focus on experimental validation of positioning and mapping with a single BS, showcasing the practical feasibility while uncovering existing gaps between theoretical expectations and real-world implementation. [Paper D] delves into the fusion problem involving mapping and SLAM results from various sources, presenting an RFS-based fusion solution.
40.
Gebresenbet, Girma
(författare)
Optimizing the performance of a wheeled mobile robots for use in agriculture using a linear-quadratic regulator
2024
Ingår i: Robotics and Autonomous Systems. - 0921-8890. ; 174
Tidskriftsartikel (refereegranskat) abstract
Use of wheeled mobile robot systems could be crucial in addressing some of the future issues facing agriculture. However, robot systems on wheels are currently unstable and require a control mechanism to increase stability, resulting in much research requirement to develop an appropriate controller algorithm for wheeled mobile robot systems. Proportional, integral, derivative (PID) controllers are currently widely used for this purpose, but the PID approach is frequently inappropriate due to disruptions or fluctuations in parameters. Other control approaches, such as linear-quadratic regulator (LQR) control, can be used to address some of the issues associated with PID controllers. In this study, a kinematic model of a four-wheel skid-steering mobile robot was developed to test the functionality of LQR control. Three scenarios (control cheap, non -zero state expensive; control expensive, non -zero state cheap; only non -zero state expensive) were examined using the characteristics of the wheeled mobile robot. Peak time, settling time, and rising time for cheap control based on these scenarios was found to be 0.1 s, 7.82 s, and 4.39 s, respectively.
41.
Giacomossi, L., et al.
(författare)
Cooperative Search and Rescue with Drone Swarm
2024
Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 381-393
Konferensbidrag (refereegranskat) abstract
Unmanned Aerial Vehicle (UAV) swarms, also known as drone swarms, have been a subject of extensive research due to their potential to enhance monitoring, surveillance, and search missions. Coordinating several drones flying simultaneously presents a challenge in increasing their level of automation and intelligence to improve strategic organization. To address this challenge, we propose a solution that uses hill climbing, potential fields, and search strategies in conjunction with a probability map to coordinate a UAV swarm. The UAVs are autonomous and equipped with distributed intelligence to facilitate a cooperative search application. Our results show the effectiveness of the swarm, indicating that this approach is a promising approach to addressing this problem.
42.
Gillet, Sarah, et al.
(författare)
Interaction-Shaping Robotics: Robots That Influence Interactions between Other Agents
2024
Ingår i: ACM Transactions on Human-Robot Interaction. - : Association for Computing Machinery (ACM). - 2573-9522. ; 13:1
Tidskriftsartikel (refereegranskat) abstract
Work in Human–Robot Interaction (HRI) has investigated interactions between one human and one robot as well as human–robot group interactions. Yet the field lacks a clear definition and understanding of the influence a robot can exert on interactions between other group members (e.g., human-to-human). In this article, we define Interaction-Shaping Robotics (ISR), a subfield of HRI that investigates robots that influence the behaviors and attitudes exchanged between two (or more) other agents. We highlight key factors of interaction-shaping robots that include the role of the robot, the robot-shaping outcome, the form of robot influence, the type of robot communication, and the timeline of the robot’s influence. We also describe three distinct structures of human–robot groups to highlight the potential of ISR in different group compositions and discuss targets for a robot’s interaction-shaping behavior. Finally, we propose areas of opportunity and challenges for future research in ISR.
43.
Gravina, Michela, et al.
(författare)
Cross-modality calibration in multi-input network for axillary lymph node metastasis evaluation
2024
Ingår i: IEEE Transactions on Artificial Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 2691-4581.
Tidskriftsartikel (refereegranskat) abstract
The use of deep neural networks (DNNs) in medical images has enabled the development of solutions characterized by the need of leveraging information coming from multiple sources, raising the Multimodal Deep Learning. DNNs are known for their ability to provide hierarchical and high-level representations of input data. This capability has led to the introduction of methods performing data fusion at an intermediate level, preserving the distinctiveness of the heterogeneous sources in modality-specific paths, while learning the way to define an effective combination in a shared representation. However, modeling the intricate relationships between different data remains an open issue. In this paper, we aim to improve the integration of data coming from multiple sources. We introduce between layers belonging to different modality-specific paths a Transfer Module (TM) able to perform the cross-modality calibration of the extracted features, reducing the effects of the less discriminative ones. As case of study, we focus on the axillary lymph nodes metastasis evaluation in malignant breast cancer, a crucial prognostic factor, affecting patient’s survival. We propose a Multi-Input Single-Output 3D Convolutional Neural Network (CNN) that considers both images acquired with multiparametric Magnetic Resonance and clinical information. In particular, we assess the proposed methodology using four architectures, namely BasicNet and three ResNet variants, showing the improvement of the performance obtained by including the TM in the network configuration. Our results achieve up to 90% and 87% of accuracy and Area under ROC curve, respectively when the ResNet10 is considered, surpassing various fusion strategies proposed in the literature.
44.
He, Yixu, et al.
(författare)
Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms
2024
Ingår i: Transportation Letters. - 1942-7867 .- 1942-7875. ; In Press
Tidskriftsartikel (refereegranskat) abstract
The application of deep reinforcement learning (DRL) techniques in intelligent transportation systems garners significant attention. In this field, reward function design is a crucial factor for DRL performance. Current research predominantly relies on a trial-and-error approach for designing reward functions, lacking mathematical support and necessitating extensive empirical experimentation. Our research uses vehicle velocity control as a case study, build training and test sets, and develop a DRL framework for speed control. This framework examines both single-objective and multi-objective optimization in reward function designs. In single-objective optimization, we introduce “expected optimal velocity” as an optimization objective and analyze how different reward functions affect performance, providing a mathematical perspective on optimizing reward functions. In multi-objective optimization, we propose a reward function design paradigm and validate its effectiveness. Our findings offer a versatile framework and theoretical guidance for developing and optimizing reward functions in DRL, particularly for intelligent transportation systems.
45.
Hernandez-Diaz, Kevin, 1992-
(författare)
Ocular Recognition in Unconstrained Sensing Environments
2024
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
This thesis focuses on the problem of increasing flexibility in the acquisition and application of biometric recognition systems based on the ocular region. While the ocular area is one of the oldest and most widely studied biometric regions thanks to its rich and discriminative elements and characteristics, most modalities such as retina, iris, eye movements, or oculomotor plant have limitations regarding data acquisition. Some require a specific type of illumination like the iris, a limited distance range like eye movements, or specific sensors and user collaboration like the retina. In this context, this thesis focuses on the periocular region, which stands out as the ocular modality with the fewest acquisition constraints. The first part focuses on using middle-layers' deep representation of pre-trained CNNs as a one-shot learning method, along with simple distance-based metrics and similarity scores for periocular recognition. This approach tackles the issue of limited data availability and collection for biometric recognition systems by eliminating the need to train the models for the target data. Furthermore, it allows seamless transitions between identification and verification scenarios with a single model, and tackles the problem of the open-world setting and training bias of CNNs. We demonstrate that off-the-shelf features from middle-layers can outperform CNNs trained for the target domain that followed a more extensive training strategy when target data is limited.The second part of the thesis analyzes traditional methods for biometric systems in the context of periocular recognition. Nowadays, these methods are often overlooked in favor of deep learning solutions. However, we show that they can still outperform heavily trained CNNs in closed-world and open-world settings and can be used in conjunction with CNNs to further improve recognition performance. Moreover, we investigate the use of the complex structure tensor as a handcrafted texture extractor at the input of CNNs. We show that CNNs can benefit from this explicit textural information in terms of performance and convergence, offering the potential for network compression and explainability of the features used. We demonstrate that CNNs may not easily access the orientation information present in the images that are exploited in some more traditional approaches.The final part of the thesis addresses the analysis of periocular recognition under different light spectra and the cross-spectral scenario. More specifically, we analyze the performance of the proposed methods under different light spectra. We also investigate the cross-spectral scenario for one-shot learning with middle-layers' deep representations and explore the possibility of bridging the domain gap in the cross-spectral scenario by training generative networks. This allows using simpler models and algorithms trained on a single spectrum.
46.
Hilger, Maximilian, 1998-, et al.
(författare)
Towards introspective loop closure in 4D radar SLAM
2024
Konferensbidrag (refereegranskat) abstract
Imaging radar is an emerging sensor modality in the context of Localization and Mapping (SLAM), especially suitable for vision-obstructed environments. This article investigates the use of 4D imaging radars for SLAM and analyzes the challenges in robust loop closure. Previous work indicates that 4D radars, together with inertial measurements, offer ample information for accurate odometry estimation. However, the low field of view, limited resolution, and sparse and noisy measurements render loop closure a significantly more challenging problem. Our work builds on the previous work - TBV SLAM - which was proposed for robust loop closure with 360∘ spinning radars. This article highlights and addresses challenges inherited from a directional 4D radar, such as sparsity, noise, and reduced field of view, and discusses why the common definition of a loop closure is unsuitable. By combining multiple quality measures for accurate loop closure detection adapted to 4D radar data, significant results in trajectory estimation are achieved; the absolute trajectory error is as low as 0.46 m over a distance of 1.8 km, with consistent operation over multiple environments.
47.
Holk, Simon, et al.
(författare)
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning
2024
Ingår i: HRI 2024 - Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. - : Association for Computing Machinery (ACM). ; , s. 259-268
Konferensbidrag (refereegranskat) abstract
Preference-based reinforcement learning (RL) has emerged as a new field in robot learning, where humans play a pivotal role in shaping robot behavior by expressing preferences on different sequences of state-action pairs. However, formulating realistic policies for robots demands responses from humans to an extensive array of queries. In this work, we approach the sample-efficiency challenge by expanding the information collected per query to contain both preferences and optional text prompting. To accomplish this, we leverage the zero-shot capabilities of a large language model (LLM) to reason from the text provided by humans. To accommodate the additional query information, we reformulate the reward learning objectives to contain flexible highlights - state-action pairs that contain relatively high information and are related to the features processed in a zero-shot fashion from a pretrained LLM. In both a simulated scenario and a user study, we reveal the effectiveness of our work by analyzing the feedback and its implications. Additionally, the collective feedback collected serves to train a robot on socially compliant trajectories in a simulated social navigation landscape. We provide video examples of the trained policies at https://sites.google.com/view/rl-predilect.
48.
Huang, Chuan, et al.
(författare)
MAINS : A Magnetic-Field-Aided Inertial Navigation System for Indoor Positioning
2024
Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 24:9, s. 15156-15166
Tidskriftsartikel (refereegranskat) abstract
A magnetic-field-aided inertial navigation system (MAINS) for indoor navigation is proposed in this article. MAINS leverages an array of magnetometers to measure spatial variations in the magnetic field, which are then used to estimate the displacement and orientation changes of the system, thereby aiding the inertial navigation system (INS). Experiments show that MAINS significantly outperforms the stand-alone INS, demonstrating the remarkable two orders of magnitude reduction in position error. Furthermore, when compared with the state-of-the-art magnetic-field-aided navigation approach, the proposed method exhibits slightly improved horizontal position accuracy. On the other hand, it has noticeably larger vertical error on datasets with large magnetic-field variations. However, one of the main advantages of MAINS compared with the state of the art is that it enables flexible sensor configurations. The experimental results show that the position error after 2 min of navigation in most cases is less than 3 m when using an array of 30 magnetometers. Thus, the proposed navigation solution has the potential to solve one of the key challenges faced with current magnetic-field simultaneous localization and mapping (SLAM) solutions-the very limited allowable length of the exploration phase during which unvisited areas are mapped.
49.
Huang, Yi, et al.
(författare)
Prescribed performance formation control for second-order multi-agent systems with connectivity and collision constraints
2024
Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 160
Tidskriftsartikel (refereegranskat) abstract
This paper studies the distributed formation control problem of second-order multi-agent systems (MASs) with limited communication ranges and collision avoidance constraints. A novel connectivity preservation and collision-free distributed control algorithm is proposed by combining prescribed performance control (PPC) and exponential zeroing control barrier Lyapunov functions (EZCBFs). In particular, we impose the time-varying performance constraints on the relative position and velocity errors between the neighboring agents, and then a PPC-based formation control algorithm is developed such that the connectivity of the communication graph can be preserved at all times, and the prescribed transient and steady performance on the relative position and velocity error can be achieved. Subsequently, by introducing the EZCBFs method, an inequality constraint condition on the control input is derived to guarantee the collision-free formation motion. By regarding the PPC-based formation controller as a nominal input, an actual formation control input is given by solving the quadratic programming (QP) problem such that each agent achieves collision-free formation motion while guaranteeing the connectivity and prescribed performance as much as possible. Finally, numerical simulation is carried out to validate the effectiveness of the proposed algorithm.
50.
Inceoglu, Arda, et al.
(författare)
Multimodal Detection and Classification of Robot Manipulation Failures
2024
Ingår i: IEEE Robotics and Automation Letters. - Piscataway, NJ : IEEE. - 2377-3766. ; 9:2, s. 1396-1403
Tidskriftsartikel (refereegranskat) abstract
An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of the physical robot-object interaction may lead to failures in object manipulation. In this letter, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net (Inceoglu et al., 2021), a deep multimodal sensor fusion-based classifier network architecture. FINO-Net accurately detects and classifies failures from raw sensory data without any additional information on task description and scene state. In this work, we use our extended FAILURE dataset (Inceoglu et al., 2021) with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that FINO-Net is also appropriate for real-time use. © 2016 IEEE.
