Asymptotically Optimal Sampling-Based Motion Planning Methods
It addresses the problem of ensuring path-quality guarantees in autonomous robotics for researchers, but is incremental as it reviews existing work rather than presenting new findings.
This survey examines sampling-based motion planning methods that probabilistically converge to optimal solutions as computational effort increases, summarizing the assumptions behind these asymptotically optimal techniques and ongoing research.
Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.