论文标题
基于最佳抽样的运动计划方法
Asymptotically Optimal Sampling-Based Motion Planning Methods
论文作者
论文摘要
运动计划是自动机器人技术中的一个基本问题，它需要找到通往特定目标的途径，以避免障碍并考虑机器人的局限性和约束。对于此路径，通常还需要优化成本函数，例如路径长度。 持续价值的搜索空间的正式路径质量保证是研究感兴趣的积极领域。最近的结果证明，随着计算努力接近无穷大，一些基于抽样的计划方法概率地趋向于最佳解决方案。这项调查总结了这些流行的渐近最佳技术背后的假设，并介绍了有关该主题的重要研究。
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.
