Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys 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 towards 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.

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基于渐近最优采样的运动规划方法综述

运动规划是自主机器人技术中的一个基本问题。它需要找到一条到达指定目标的路径，避开障碍物并遵守机器人的限制和约束。通常希望这条路径还优化成本函数，例如路径长度。连续值搜索空间的正式路径质量保证是一个活跃的研究领域。最近的结果已经证明，随着计算工作量接近无穷大，一些基于采样的规划方法在概率上收敛于最优解。本调查总结了这些流行的渐近优化技术背后的假设，并介绍了对该主题正在进行的重要研究。