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

Content Provider	Springer Nature Link
Author	Hauser, Kris
Copyright Year	2011
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.
Starting Page	35
Ending Page	48
Page Count	14
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	32
Issue Number	1
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2011-09-15
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Motion planning Model predictive control Receding horizon control Assisted teleoperation Obstacle avoidance Pursuit-evasion Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Control, Robotics, Mechatronics Robotics and Automation
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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