Bang-Bang Boosting of RRTs
La Valle, Alexander J.; Sakcak, Basak; Lavalle, Steven M. (2023-12-13)
IEEE
13.12.2023
A. J. La Valle, B. Sakcak and S. M. LaValle, "Bang-Bang Boosting of RRTs," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 2869-2876, doi: 10.1109/IROS55552.2023.10341760
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202404262970
https://urn.fi/URN:NBN:fi:oulu-202404262970
Tiivistelmä
Abstract
This paper presents methods for dramatically improving the performance of sampling-based kinodynamic planners. The key component is a complete, exact steering method that produces a time-optimal trajectory between any states for a vector of synchronized double integrators. This method is applied in three ways: 1) to generate RRT edges that quickly solve the two-point boundary-value problems, 2) to produce a (quasi)metric for more accurate Voronoi bias in RRTs, and 3) to iteratively time-optimize a given collision-free trajectory. Experiments are performed for state spaces with up to 2000 dimensions, resulting in improved computed trajectories and orders of magnitude computation time improvements over using ordinary metrics and constant controls.
This paper presents methods for dramatically improving the performance of sampling-based kinodynamic planners. The key component is a complete, exact steering method that produces a time-optimal trajectory between any states for a vector of synchronized double integrators. This method is applied in three ways: 1) to generate RRT edges that quickly solve the two-point boundary-value problems, 2) to produce a (quasi)metric for more accurate Voronoi bias in RRTs, and 3) to iteratively time-optimize a given collision-free trajectory. Experiments are performed for state spaces with up to 2000 dimensions, resulting in improved computed trajectories and orders of magnitude computation time improvements over using ordinary metrics and constant controls.
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