| 1. |
- Armleder, Simon, et al.
(författare)
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Interactive Force Control Based on Multimodal Robot Skin for Physical Human-Robot Collaboration
- 2021
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Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : Wiley. - 2640-4567. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- This work proposes and realizes a control architecture that can support the deployment of a large-scale robot skin in a Human-Robot Collaboration scenario. It is shown, how whole-body tactile feedback can extend the capabilities of robots during dynamic interactions by providing information about multiple contacts across the robot's surface. Specifically, an uncalibrated skin system is used to implement stable force control while simultaneously handling the multi-contact interactions of a user. The system formulates control tasks for force control, tactile guidance, collision avoidance, and compliance, and fuses them with a multi-priority redundancy resolution strategy. The approach is evaluated on an omnidirectional mobile-manipulator with dual arms covered with robot skin. Results are assessed under dynamic conditions, showing that multi-modal tactile information enables robust force control while at the same time remaining responsive to a user's interactions.
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| 2. |
- Armleder, Simon, et al.
(författare)
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Tactile-Based Negotiation of Unknown Objects during Navigation in Unstructured Environments with Movable Obstacles
- 2024
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Ingår i: Advanced Intelligent Systems. - 2640-4567. ; 6:3
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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.
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| 3. |
- Athle, Robin, et al.
(författare)
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Ferroelectric Tunnel Junction Memristors for In-Memory Computing Accelerators
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Ingår i: Advanced Intelligent Systems. - 2640-4567.
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Tidskriftsartikel (refereegranskat)abstract
- Neuromorphic computing has seen great interest as leaps in artificial intelligence (AI) applications have exposed limitations due to heavy memory access, with the von Neumann computing architecture. The parallel in-memory computing provided by neuromorphic computing has the potential to significantly improve latency and power consumption. Key to analog neuromorphic computing hardware are memristors, providing non-volatile multistate conductance levels, high switching speed, and energy efficiency. Ferroelectric tunnel junction (FTJ) memristors are prime candidates for this purpose, but the impact of the particular characteristics for their performance upon integration into large crossbar arrays, the core compute element for both inference and training in deep neural networks, requires close investigation. In this work, a W/HfxZr1−xO2/TiN FTJ with 60 programmable conductance states, a dynamic range (DR) up to 10, current density >3 A m−2 at V read = 0.3 V and highly nonlinear current–voltage (I–V) characteristics (>1100) is experimentally demonstrated. Using a circuit macro-model, the system level performance of a true crossbar array is evaluated and a 92% classification accuracy of the modified nation institute of science and technology (MNIST) dataset is achieved. Finally, the low on conductance in combination with the highly nonlinear I–V characteristics enable the realization of large selector-free crossbar arrays for neuromorphic hardware accelerators.
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| 4. |
- Escobar Teran, Freddy, et al.
(författare)
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Enhancing the Conductivity of the Poly(3,4-ethylenedioxythiophene)-Poly(styrenesulfonate) Coating and Its Effect on the Performance of Yarn Actuators
- 2020
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Ingår i: Advanced Intelligent Systems. - : Wiley-Blackwell. - 2640-4567. ; 2:5
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Tidskriftsartikel (refereegranskat)abstract
- Nonconductive commercial viscose yarns have been coated with a commercial conducting poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) layer providing electrical conductivity which allowed a second coating of the electroactive conducting polymer polypyrrole through electropolymerization to develop textile yarns actuators. To simplify the PEDOT coating process and at the same time make this process more suitable for application in industry, a new coating method is developed and the properties of the PEDOT-PSS conducting layer is optimized, paying attention on its effect on the actuation performance. The effect of the concentration of an additive such as dimethylsulfoxide (DMSO) on actuation, and of PEDOT:PSS layers, is investigated. Results show that on improving this conducting layer, better performance than previously developed yarn-actuators is obtained, with strains up to 0.6%. This study provides a simple and efficient fabrication method toward soft, textile-based actuators for wearables and assistive devices with improved features.
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| 5. |
- Héraly, Frédéric, et al.
(författare)
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Nanodancing with Moisture : Humidity-Sensitive Bilayer Actuator Derived from Cellulose Nanofibrils and Reduced Graphene Oxide
- 2022
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Ingår i: Advanced Intelligent Systems. - : Wiley. - 2640-4567. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Bilayer actuators, traditionally consisting of two laminated materials, are the most common types of soft or hybrid actuators. Herein, a nanomaterial-based organic–inorganic humidity-sensitive bilayer actuator composed of TEMPO-oxidized cellulose nanofibrils (TCNF-Na+) and reduced graphene oxide (rGO) sheets is presented. The hybrid actuator displays a large humidity-driven locomotion with an atypical fast unbending. Cationic exchange of the anionically charged TCNF-Na+ and control of the layer thickness is used to tune and dictate the locomotion and actuator's response to humidity variations. Assembly of a self-oscillating electrical circuit, that includes a conductive rGO layer, displays autonomous on-and-off lighting in response to actuation-driven alternating electrical heating.
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| 6. |
- Kong, Depeng, et al.
(författare)
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Bioinspired Co-Design of Tactile Sensor and Deep Learning Algorithm for Human-Robot Interaction
- 2022
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Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : Wiley. - 2640-4567. ; 4:6
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Tidskriftsartikel (refereegranskat)abstract
- Robots equipped with bionic skins for enhancing the robot perception capability are increasingly deployed in wide applications ranging from healthcare to industry. Artificial intelligence algorithms that can provide bionic skins with efficient signal processing functions further accelerate the development of this trend. Inspired by the somatosensory processing hierarchy of humans, the bioinspired co-design of a tactile sensor and a deep learning-based algorithm is proposed herein, simplifying the sensor structure while providing computation-enhanced tactile sensing performance. The soft piezoresistive sensor, based on the carbon black-coated polyurethane sponge, offers a continuous sensing area. By utilizing a customized deep neural network (DNN), it can detect external tactile stimulus spatially continuously. Besides, a novel data augmentation method is developed based on the sensor's hexagonal structure that has a sixfold rotation symmetry. It can significantly enhance the generalization ability of the DNN model by enriching the collected training data with generated pseudo-data. The functionality of the sensor and the robustness of the proposed data augmentation strategy are verified by precisely recognizing five touch modalities, illustrating a well-generalized performance, and providing a promising application prospect in human-robot interaction.
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| 7. |
- Mohammadi, Mohsen, et al.
(författare)
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Versatile Ultrasoft Electromagnetic Actuators with Integrated Strain-Sensing Cellulose Nanofibril Foams
- 2023
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Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : WILEY. - 2640-4567. ; 5:7
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Tidskriftsartikel (refereegranskat)abstract
- As robots more frequently fraternize with humans in everyday life, aspects such as safety, flexibility of tasks, and appearance become increasingly important. Soft robotics is attractive for new human-close applications, but soft actuators constitute a major challenge both in terms of actuation force and speed, and in terms of control and accuracy of the deformable soft actuator body. Herein, several of these challenges are addressed by developing versatile ultrasoft electromagnetic actuators that operate in absence of external magnetic fields, while simultaneously monitoring their states by internal strain sensors. The versatile actuators can compress to less than 50% of their initial length with strain-independent contraction force and operate in both contraction and expansion modes up to 200 Hz frequency while conforming to curved surfaces. The soft multilayer conductive cellulose-based foams are lightweight (3 mg cm(-3)) and provide internal strain-sensing capability and structural support, thereby improving the monitoring and controllability of the actuators while maintaining an axial softness of 0.6 kPa. It is believed that the concept of soft versatile electromagnetic actuators with integrated lightweight strain-sensing foams is promising for a wide range of applications within soft robotics.
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| 8. |
- Zhao, Ziwen, et al.
(författare)
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Automated Analysis of Nano-Impact Single-Entity Electrochemistry Signals Using Unsupervised Machine Learning and Template Matching
- 2024
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Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : John Wiley & Sons. - 2640-4567. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Nano-impact (NIE) (also referred to as collision) single-entity electrochemistry is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To obtain meaningful information from NIE experiments, analysis and feature extraction on large datasets are necessary. Herein, a method is developed for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications. Nano-impact (NIE) electrochemistry is an emerging technique for studying individual entities. Analyzing large NIE datasets, often with low signal-to-noise ratios, is challenging. Herein, an automated approach is introduced using unsupervised machine learning and template matching for accurate feature extraction from spike-shaped NIE signals. It improves data processing, accuracy and standardization, reducing human bias in signal interpretation across experiments.image (c) 2023 WILEY-VCH GmbH
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| 9. |
- Zhu, Jingyuan, et al.
(författare)
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Solving the 3-Satisfiability Problem Using Network-Based Biocomputation
- 2022
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Ingår i: Advanced Intelligent Systems. - : John Wiley & Sons. - 2640-4567. ; 4:12
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Tidskriftsartikel (refereegranskat)abstract
- The 3-satisfiability Problem (3-SAT) is a demanding combinatorial problem that is of central importance among the nondeterministic polynomial (NP) complete problems, with applications in circuit design, artificial intelligence, and logistics. Even with optimized algorithms, the solution space that needs to be explored grows exponentially with the increasing size of 3-SAT instances. Thus, large 3-SAT instances require excessive amounts of energy to solve with serial electronic computers. Network-based biocomputation (NBC) is a parallel computation approach with drastically reduced energy consumption. NBC uses biomolecular motors to propel cytoskeletal filaments through nanofabricated networks that encode mathematical problems. By stochastically exploring possible paths through the networks, the cytoskeletal filaments find possible solutions. However, to date, no NBC algorithm for 3-SAT has been available. Herein, an algorithm that converts 3-SAT into an NBC-compatible network format is reported and four small 3-SAT instances (with up to three variables and five clauses) using the actin-myosin biomolecular motor system are experimentally solved. Because practical polynomial conversions to 3-SAT exist for many important NP complete problems, the result opens the door to enable NBC to solve small instances of a wide range of problems.
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