Efficient Multi-Robot Kinodynamic Motion Planning with db-CBS
The author presents db-CBS, an efficient motion planner for multi-robot systems that considers dynamics and control bounds. The approach combines CBS and db-A* to find near-optimal solutions quickly.
iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization
Combining motion primitives, bounded discontinuity, and trajectory optimization in iDb-RRT for efficient kinodynamic motion planning.
Efficient Motion Planning for Mobile Robots with Non-Trivial Dynamics using Learned Controllers and Roadmaps with Gaps
This work proposes a decoupled strategy that first trains a goal-conditioned controller offline in an empty environment to deal with the robot's dynamics, and then constructs a "Roadmap with Gaps" to approximately learn how to solve planning queries in a target environment using the learned controller. The roadmap guidance is integrated with an asymptotically optimal tree sampling-based planner to achieve improved computational efficiency for motion planning.
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