Fully Connected Informed Trees (FCIT*)
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Tyler and Jonathan have been working with Wil Thomason (The AI Institute), Zak Kingston (CoMMA Lab, Purdue University), and Lydia Kavraki (Kavraki Lab, Rice University) on extending their exciting work on vector accelerated motion planning (VAMP). Fully Connected Informed Trees (FCIT*) uses SIMD instructions to achieve real-time almost-surely asymptotically optimal planning and has been submitted to ICRA 2025. If you'd like to know more you can watch the trailer video on YouTube, read the paper on arXiv, and checkout the code.
- Publication
- Conference
- Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
- Date
- Notes
- Submitted. Manuscript #2982
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.