| 1. |
- 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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| 2. |
- Heiden, Eric, et al.
(författare)
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Bench-MR : A Motion Planning Benchmark for Wheeled Mobile Robots
- 2021
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 6:3, s. 4536-4543
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Tidskriftsartikel (refereegranskat)abstract
- Planning smooth and energy-efficient paths for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, several sampling-based motion-planning algorithms, extend functions and post-smoothing algorithms have been introduced for such motion-planning systems. Choosing the best combination of components for an application is a tedious exercise, even for expert users. We therefore present Bench-MR, the first open-source motion-planning benchmarking framework designed for sampling-based motion planning for nonholonomic, wheeled mobile robots. Unlike related software suites, Bench-MR is an easy-to-use and comprehensive benchmarking framework that provides a large variety of sampling-based motion-planning algorithms, extend functions, collision checkers, post-smoothing algorithms and optimization criteria. It aids practitioners and researchers in designing, testing, and evaluating motion-planning systems, and comparing them against the state of the art on complex navigation scenarios through many performance metrics. Through several experiments, we demonstrate how Bench-MR can be used to gain extensive insights from the benchmarking results it generates.
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| 3. |
- Kucner, Tomasz Piotr, PhD, 1988-, et al.
(författare)
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Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots
- 2020
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Bok (övrigt vetenskapligt/konstnärligt)abstract
- This book describes how robots can make sense of motion in their surroundings and use the patterns they observe to blend in better in dynamic environments shared with humans.The world around us is constantly changing. Nonetheless, we can find our way and aren’t overwhelmed by all the buzz, since motion often follows discernible patterns. Just like humans, robots need to understand the patterns behind the dynamics in their surroundings to be able to efficiently operate e.g. in a busy airport. Yet robotic mapping has traditionally been based on the static world assumption, which disregards motion altogether. In this book, the authors describe how robots can instead explicitly learn patterns of dynamic change from observations, store those patterns in Maps of Dynamics (MoDs), and use MoDs to plan less intrusive, safer and more efficient paths. The authors discuss the pros and cons of recently introduced MoDs and approaches to MoD-informed motion planning, and provide an outlook on future work in this emerging, fascinating field.
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| 4. |
- Palmieri, Luigi, et al.
(författare)
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Kinodynamic Motion Planning on Gaussian Mixture Fields
- 2017
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Ingår i: IEEE International Conference on Robotics and Automation (ICRA 2017). - : IEEE. ; , s. 6176-6181
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Konferensbidrag (refereegranskat)abstract
- We present a mobile robot motion planning ap-proach under kinodynamic constraints that exploits learnedperception priors in the form of continuous Gaussian mixturefields. Our Gaussian mixture fields are statistical multi-modalmotion models of discrete objects or continuous media in theenvironment that encode e.g. the dynamics of air or pedestrianflows. We approach this task using a recently proposed circularlinear flow field map based on semi-wrapped GMMs whosemixture components guide sampling and rewiring in an RRT*algorithm using a steer function for non-holonomic mobilerobots. In our experiments with three alternative baselines,we show that this combination allows the planner to veryefficiently generate high-quality solutions in terms of pathsmoothness, path length as well as natural yet minimum controleffort motions through multi-modal representations of Gaussianmixture fields.
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| 5. |
- Rudenko, Andrey, 1991-, et al.
(författare)
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Human Motion Prediction under Social Grouping Constraints
- 2018
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Ingår i: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. - 9781538680940 - 9781538680957 ; , s. 3358-3364
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Konferensbidrag (refereegranskat)abstract
- Accurate long-term prediction of human motion inpopulated spaces is an important but difficult task for mobile robots and intelligent vehicles. What makes this task challenging is that human motion is influenced by a large variety offactors including the person’s intention, the presence, attributes, actions, social relations and social norms of other surrounding agents, and the geometry and semantics of the environment. In this paper, we consider the problem of computing human motion predictions that account for such factors. We formulate the task as an MDP planning problem with stochastic policies and propose a weighted random walk algorithm in which each agent is locally influenced by social forces from other nearby agents. The novelty of this paper is that we incorporate social grouping information into the prediction process reflecting the soft formation constraints that groups typically impose to their members’ motion. We show that our method makes more accurate predictions than three state-of-the-art methods in terms of probabilistic and geometrical performance metrics.
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| 6. |
- Swaminathan, Chittaranjan Srinivas, 1991-, et al.
(författare)
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Benchmarking the utility of maps of dynamics for human-aware motion planning
- 2022
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Ingår i: Frontiers in Robotics and AI. - : Frontiers Media S.A.. - 2296-9144. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.
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| 7. |
- Swaminathan, Chittaranjan Srinivas, 1991-, et al.
(författare)
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Down the CLiFF : Flow-Aware Trajectory Planning under Motion Pattern Uncertainty
- 2018
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Ingår i: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538680940 - 9781538680957 ; , s. 7403-7409
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Konferensbidrag (refereegranskat)abstract
- In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines.
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