Asymptotically Optimal Sampling-Based Motion Planning Methods

Motion planning is a fundamental problem in autonomous robotics that requires finding path to specified goal avoids obstacles and takes into account robot's limitations constraints. It often desirable for this also optimize cost function, such as length. Formal path-quality guarantees continuously valued search spaces are an active area of research interest. Recent results have proven some sampling-based methods probabilistically converge toward the optimal solution computational effort approaches infinity. This article summarizes assumptions behind these popular asymptotically techniques provides introduction significant ongoing on topic.