article

On responsiveness, safety, and completeness in real-time motion planning

Author:

Kris HauserAuthors Info & Claims

Autonomous Robots, Volume 32, Issue 1

Pages 35 - 48

https://doi.org/10.1007/s10514-011-9254-z

Published: 01 January 2012 Publication History

Abstract

Replanning is a powerful mechanism for controlling robot motion under hard constraints and unpredictable disturbances, but it involves an inherent tradeoff between the planner's power (e.g., a planning horizon or time cutoff) and its responsiveness to disturbances. This paper presents an adaptive time-stepping architecture for real-time planning with several advantageous properties. By dynamically adapting to the amount of time needed for a sample-based motion planner to make progress toward the goal, the technique is robust to the typically high variance exhibited by replanning queries. The technique is proven to be safe and asymptotically complete in a deterministic environment and a static objective. For unpredictably moving obstacles, the technique can be applied to keep the robot safe more reliably than reactive obstacle avoidance or fixed time-step replanning. It can also be applied in a contingency planning algorithm that achieves simultaneous safety-seeking and goal-seeking motion. These techniques generate responsive and safe motion in both simulated and real robots across a range of difficulties, including applications to bounded-acceleration pursuit-evasion, indoor navigation among moving obstacles, and aggressive collision-free teleoperation of an industrial robot arm.

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Cited By

Zhang RYu CChen JFan CGao SKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Learning-based motion planning in dynamic environments using GNNs and temporal encodingProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602445(30003-30015)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602445
Rakita DShi HMutlu BGleicher M(2021)CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance2021 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA48506.2021.9561505(9995-10001)Online publication date: 30-May-2021
https://dl.acm.org/doi/10.1109/ICRA48506.2021.9561505
Rakita DMutlu BGleicher M(2020)An analysis of RelaxedIK: an optimization-based framework for generating accurate and feasible robot arm motionsAutonomous Robots10.1007/s10514-020-09918-944:7(1341-1358)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10514-020-09918-9
Show More Cited By

On responsiveness, safety, and completeness in real-time motion planning
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Planning and scheduling

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Published In

cover image Autonomous Robots

Autonomous Robots Volume 32, Issue 1

January 2012

94 pages

ISSN:0929-5593

Issue’s Table of Contents

Copyright © Copyright © 2012 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2012

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Cited By

Zhang RYu CChen JFan CGao SKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Learning-based motion planning in dynamic environments using GNNs and temporal encodingProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602445(30003-30015)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602445
Rakita DShi HMutlu BGleicher M(2021)CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance2021 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA48506.2021.9561505(9995-10001)Online publication date: 30-May-2021
https://dl.acm.org/doi/10.1109/ICRA48506.2021.9561505
Rakita DMutlu BGleicher M(2020)An analysis of RelaxedIK: an optimization-based framework for generating accurate and feasible robot arm motionsAutonomous Robots10.1007/s10514-020-09918-944:7(1341-1358)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10514-020-09918-9
Petschnigg CBrandstötter MPichler HHofbaur MDieber B(2019)Quantum Computation in Robotic Science and Applications2019 International Conference on Robotics and Automation (ICRA)10.1109/ICRA.2019.8793768(803-810)Online publication date: 20-May-2019
https://dl.acm.org/doi/10.1109/ICRA.2019.8793768
Chen YHe ZLi S(2019)Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environmentAutonomous Robots10.1007/s10514-019-09879-843:8(2271-2292)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10514-019-09879-8
Bowen CAlterovitz R(2018)Closed-Loop Global Motion Planning for Reactive, Collision-Free Execution of Learned TasksACM Transactions on Human-Robot Interaction10.1145/32090457:1(1-16)Online publication date: 21-May-2018
https://dl.acm.org/doi/10.1145/3209045
Pan ZManocha D(2017)Feedback motion planning for liquid pouring using supervised learning2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2017.8202300(1252-1259)Online publication date: 24-Sep-2017
https://dl.acm.org/doi/10.1109/IROS.2017.8202300
Otte MFrazzoli E(2016)RRTXInternational Journal of Robotics Research10.1177/027836491559467935:7(797-822)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1177/0278364915594679
Xu BMin H(2016)Solving minimum constraint removal (MCR) problem using a social-force-model-based ant colony algorithmApplied Soft Computing10.1016/j.asoc.2016.02.04743:C(553-560)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.02.047
Park CPan JManocha D(2012)ITOMPProceedings of the Twenty-Second International Conference on International Conference on Automated Planning and Scheduling10.5555/3038546.3038571(207-215)Online publication date: 25-Jun-2012
https://dl.acm.org/doi/10.5555/3038546.3038571

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