Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)

TL;DR

FCIT* is a novel motion planning algorithm that eliminates the need for nearest-neighbour searches by fully connecting the graph, leveraging SIMD parallelism to improve initial solution times and asymptotic optimality.

Contribution

It introduces FCIT*, the first fully connected, informed, anytime asymptotically optimal motion planner that removes the costly nearest-neighbour operations.

Findings

Faster initial solutions than state-of-the-art algorithms.

Converges towards optimal plans in an anytime manner.

Reduces computational cost by removing nearest-neighbour structures.

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.