RA-L: Asymptotically Optimal RRT-Connect (AORRTC)
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Tyler's paper on asymptotically optimal RRT-Connect (AORRTC) has been published in RA-L. We'll be presenting it with Wil Thomason and Zak Kingston (CoMMA Lab, Purdue University) at ICRA 2026 and are looking forward to seeing everyone in Vienna in June 2026. The paper is already up on on arXiv with an associated video on YouTube and the algorithm available in both VAMP and OMPL.
- Publication
- Journal
- IEEE Robotics and Automation Letters (RA-L)
- Volume
- 10
- Number
- 12
- Pages
- 13375–13382
- Date
Abstract
Finding high-quality solutions quickly is an important objective in motion planning. This is especially true for high-degree-of-freedom robots. Satisficing planners have traditionally found feasible solutions quickly but provide no guarantees on their optimality, while almost-surely asymptotically optimal (a.s.a.o.) planners have probabilistic guarantees on their convergence towards an optimal solution but are more computationally expensive.
This paper uses the AO-x meta-algorithm to extend the satisficing RRT-Connect planner to optimal planning. The resulting Asymptotically Optimal RRT-Connect (AORRTC) finds initial solutions in similar times as RRT-Connect and uses additional planning time to converge towards the optimal solution in an anytime manner. It is proven to be probabilistically complete and a.s.a.o.
AORRTC was tested with the Panda (7 DoF) and Fetch (8 DoF) robotic arms on the MotionBenchMaker dataset. These experiments show that AORRTC finds initial solutions as fast as RRT-Connect and faster than the tested state-of-the-art a.s.a.o. algorithms while converging to better solutions faster. AORRTC finds solutions to difficult high-DoF planning problems in milliseconds where the other a.s.a.o. planners could not consistently find solutions in seconds. This performance was demonstrated both with and without single instruction/multiple data (SIMD) acceleration.