iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization¶
Authors: Joaquim Ortiz-Haro, Wolfgang Hönig, Valentin N. Hartmann, Marc Toussaint, Ludovic Righetti
Published: 2024 (Conference Paper)
Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Algorithm: iDb-RRT
arXiv: 2403.10745
DOI: 10.1109/IROS58592.2024.10802168
Summary¶
Combines short motion primitives (allowing bounded discontinuities) with the RRT exploration strategy, iteratively repairing discontinuities via trajectory optimization. Finds solutions up to 10X faster than prior methods across a benchmark of 30 problems.
Abstract¶
Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic constraints makes the motion planning problem significantly harder, as it requires solving two-value boundary problems (computationally expensive) or propagating random control inputs (uninformative). Alternatively, Iterative Discontinuity Bounded A* (iDb-A*), introduced in our previous study, combines search and optimization iteratively. The search step connects short trajectories (motion primitives) while allowing a bounded discontinuity between the motion primitives, which is later repaired in the trajectory optimization step. Building upon these foundations, in this paper, we present iDb-RRT, a sampling-based kinodynamic motion planning algorithm that combines motion primitives and trajectory optimization within the RRT framework. iDb-RRT is probabilistically complete and can be implemented in forward or bidirectional mode.
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Tags¶
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Kinodynamic planning
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RRT
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iDb-RRT
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Motion primitives
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Trajectory optimization
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Discontinuity bounded