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| 2. |
- Almeida, Tiago, 1996-, et al.
(författare)
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THÖR-Magni : Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction
- 2023
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Ingår i: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). - : IEEE. - 9798350307450 - 9798350307443 ; , s. 2192-2201
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Konferensbidrag (refereegranskat)abstract
- Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics.
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| 3. |
- Falcone, Paolo, 1977, et al.
(författare)
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Effects of Roll Dynamics in Model Predictive Control for Autonomous Vehicles
- 2008
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Ingår i: 47th IEEE Conference on Decision and Control, December 9-11, 2008, Fiesta Americana Grand Coral Beach, Cancun, Mexico.
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Konferensbidrag (refereegranskat)abstract
- A Model Predictive Control (MPC) approach for autonomous vehicles is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle. We start from the results presented in [4] and [7], where the MPC problem formulationrelies on a simple bicycle model, and reformulate the problem by using a more complex vehicle model including roll dynamics. We present and discuss simulations of a vehicle performing high speed double lane change maneuvers where roll dynamics become relevant. The results demonstrate that the proposed model based design is able to effectively stabilize the vehicle by using a three dimensional vehicle model at the cost of a highercomputational load.
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| 4. |
- Heuer, Lukas, 1992-, et al.
(författare)
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Proactive Model Predictive Control with Multi-Modal Human Motion Prediction in Cluttered Dynamic Environments
- 2023
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Ingår i: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 October 2023, Detroit, MI, USA. - : IEEE. - 9781665491914 - 9781665491907 ; , s. 229-236
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Konferensbidrag (refereegranskat)abstract
- For robots navigating in dynamic environments, exploiting and understanding uncertain human motion prediction is key to generate efficient, safe and legible actions. The robot may perform poorly and cause hindrances if it does not reason over possible, multi-modal future social interactions. With the goal of enhancing autonomous navigation in cluttered environments, we propose a novel formulation for nonlinear model predictive control including multi-modal predictions of human motion. As a result, our approach leads to less conservative, smooth and intuitive human-aware navigation with reduced risk of collisions, and shows a good balance between task efficiency, collision avoidance and human comfort. To show its effectiveness, we compare our approach against the state of the art in crowded simulated environments, and with real-world human motion data from the THOR dataset. This comparison shows that we are able to improve task efficiency, keep a larger distance to humans and significantly reduce the collision time, when navigating in cluttered dynamic environ-ments. Furthermore, the method is shown to work robustly with different state-of-the-art human motion predictors.
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| 5. |
- Kucner, Tomasz Piotr, et al.
(författare)
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Survey of maps of dynamics for mobile robots
- 2023
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Ingår i: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 42:11, s. 977-1006
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Tidskriftsartikel (refereegranskat)abstract
- Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.
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| 6. |
- Molina, Sergi, et al.
(författare)
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The ILIAD Safety Stack : Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots
- 2023
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Ingår i: IEEE robotics & automation magazine. - : IEEE. - 1070-9932 .- 1558-223X.
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Tidskriftsartikel (refereegranskat)abstract
- Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.
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| 7. |
- Palmieri, Luigi, et al.
(författare)
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Dispertio : Optimal Sampling For Safe Deterministic Motion Planning
- 2020
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 5:2, s. 362-368
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Tidskriftsartikel (refereegranskat)abstract
- A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for sampling-based robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.
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| 8. |
- Palmieri, Luigi, et al.
(författare)
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Guest Editorial : Introduction to the Special Issue on Long-Term Human Motion Prediction
- 2021
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE Press. - 2377-3766. ; 6:3, s. 5613-5617
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The articles in this special section focus on long term human motion prediction. This represents a key ability for advanced autonomous systems, especially if they operate in densely crowded and highly dynamic environments. In those settings understanding and anticipating human movements is fundamental for robust long-term operation of robotic systems and safe human-robot collaboration. Foreseeing how a scene with multiple agents evolves over time and incorporating predictions in a proactive manner allows for novel ways of planning and control, active perception, or humanrobot interaction. Recent planning and control approaches use predictive techniques to better cope with the dynamics of the environment, thus allowing the generation of smoother and more legible robot motion. Predictions can be provided as input to the planning or optimization algorithm (e.g. as a cost term or heuristic function), or as additional dimension to consider in the problem formulation (leading to an increased computational complexity). Recent perception techniques deeply interconnect prediction modules with detection, segmentation and tracking, to generally increase the accuracy of different inference tasks, i.e. filtering, predicting. As also indicated by some of the scientific works accepted in this special issue, novel deep learning architectures allow better interleaving of the aforementioned units.
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| 9. |
- Rudenko, Andrey, 1991-, et al.
(författare)
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Human motion trajectory prediction : a survey
- 2020
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Ingår i: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 39:8, s. 895-935
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Tidskriftsartikel (refereegranskat)abstract
- With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.
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| 10. |
- Rudenko, Andrey, 1991-, et al.
(författare)
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Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments
- 2021
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 6:2, s. 3248-3255
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Tidskriftsartikel (refereegranskat)abstract
- Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from demonstrations with on-site collected trajectory data is a powerful approach to discover recurrent motion patterns, generalization to new environments, where sufficient motion data are not readily available, remains a challenge. In many cases, however, semantic information about the environment is a highly informative cue for the prediction of pedestrian motion or the estimation of collision risks. In this work, we infer occupancy priors of human motion using only semantic environment information as input. To this end, we apply and discuss a traditional Inverse Optimal Control approach, and propose a novel approach based on Convolutional Neural Networks (CNN) to predict future occupancy maps. Our CNN method produces flexible context-aware occupancy estimations for semantically uniform map regions and generalizes well already with small amounts of training data. Evaluated on synthetic and real-world data, it shows superior results compared to several baselines, marking a qualitative step-up in semantic environment assessment.
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