FLASH-CBF-RRT: Fast Lipschitz-Adaptive Safe Horizon CBF Steering for Certified Motion Planning
We develop a method to accelerate CBF steering by computing a time horizon within which CBF-filtered velocity may be applied constantly using closed-form kinematic bounds on collision-clearance.
ProofPaper ↗
Key points
- Fast motion planning requires both sample-efficient exploration and computationally efficient local steering.
- Quadratic program controllers with control barrier function (CBF) constraints can be used to achieve effective local steering but can increase the planning time due to the expensive sequential evaluations.
- Our method, FLASH-CBF-RRT, significantly reduces the number of evaluations compared to prior CBF-RRT approaches because the planner can take adaptive steering step sizes that are shorter near obstacles and longer in open space.
- Across 689 MotionBenchMaker problems, FLASH-CBF-RRT matches RRT-Connect's 100% success rate while reducing median planning time by 3.82x, and is 3.88x faster than fixed-step CBF-RRT.
Sources (1)
- [1]FLASH-CBF-RRT: Fast Lipschitz-Adaptive Safe Horizon CBF Steering for Certified Motion PlanningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 07:22 PM
We develop a method to accelerate CBF steering by computing a time horizon within which CBF-filtered velocity may be applied constantly using closed-form kinematic bounds on collision-clearance.
Fast motion planning requires both sample-efficient exploration and computationally efficient local steering.
Extractive summary: sentences quoted from the sources.
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