Robotics 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.

Efficient Motion Planning for Manipulators using Control Barrier Function-Induced Neural Controller

Efficient Motion Planning for Mobile Robots with Non-Trivial Dynamics using Learned Controllers and Roadmaps with Gaps