ESP @ ICRA 2025 — Asymptotically Optimal Planning
Share
Tyler Wilson presented his work on Fully Connected Informed Trees (FCIT*) at ICRA 2025 in Atlanta, Georgia. You can already read the paper on arXiv, watch a short video on YouTube, and checkout the code yourself.
While there, Tyler also presented an early preview of our recent RA-L submission on asymptotically optimal RRT-Connect (AORRTC), which you can read more about here.
- Publication
- Conference
- Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
- Location
- Atlanta, GA, USA
- Date
Abstract
Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. Traditionally, and especially for asymptotically optimal sampling-based motion planners, the most expensive operations are local motion validation and querying the nearest neighbours of a configuration.
Recent advances have significantly reduced the cost of motion validation by using single instruction/multiple data (SIMD) parallelism to improve solution times for satisficing motion planning problems. These advances have not yet been applied to asymptotically optimal motion planning.
This paper presents Fully Connected Informed Trees (FCIT*), the first fully connected, informed, anytime almost-surely asymptotically optimal (ASAO) algorithm. FCIT* exploits the radically reduced cost of edge evaluation via SIMD parallelism to build and search fully connected graphs. This removes the need for nearest-neighbours structures, which are a dominant cost for many sampling-based motion planners, and allows it to find initial solutions faster than state-of-the-art ASAO (VAMP, OMPL) and satisficing (OMPL) algorithms on the MotionBenchMaker dataset while converging towards optimal plans in an anytime manner.