iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization
<|reference_start|>iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization: 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. We have tested our algorithm across a benchmark suite comprising 30 problems, spanning 8 different systems, and shown that iDb-RRT can find solutions up to 10x faster than previous methods, especially in complex scenarios that require long trajectories or involve navigating through narrow passages.<|reference_end|>
@article{ortiz-haro2024idb-rrt:,
    title={iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion
  Primitives and Trajectory Optimization},
    author={Joaquim Ortiz-Haro, Wolfgang H"onig, Valentin N. Hartmann, Marc
  Toussaint, Ludovic Righetti},
    journal={arXiv preprint arXiv:2403.10745},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10745},
    primaryClass={cs.RO}
}