
This work presents a control barrier function-induced neural controller (CBF-INC) that can be integrated into sampling-based motion planning algorithms to efficiently explore the configuration space while ensuring safety.


coremsg

efficient-motion-planning-for-manipulators-using-control-barrier-function-induced-neural-controller


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


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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-mobile-robots-with-non-trivial-dynamics-using-learned-controllers-and-roadmaps-with-gaps


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