论文标题
对更快相关区域发现的带注释的骨骼偏见运动计划
Annotated-skeleton Biased Motion Planning for Faster Relevant Region Discovery
论文作者
论文摘要
运动计划算法通常利用有关环境的拓扑信息来提高计划者的性能。但是，这些方法通常仅着眼于环境的连接性，同时忽略了其他属性，例如障碍清除，地形条件和资源可及性。我们提出了一种使用此类信息来增加代表工作区拓扑的骨骼的方法，以指导基于抽样的运动计划者，以快速发现与当前问题最相关的区域。我们的方法剥夺了指导和计划，使基本计划算法在计划过程的早期可以找到所需的途径成为可能。我们证明了方法在机器人问题和药物设计中的应用中的功效。我们的方法能够快速产生理想的路径，而不会改变基础计划者。
Motion planning algorithms often leverage topological information about the environment to improve planner performance. However, these methods often focus only on the environment's connectivity while ignoring other properties such as obstacle clearance, terrain conditions, and resource accessibility. We present a method that augments a skeleton representing the workspace topology with such information to guide a sampling-based motion planner to rapidly discover regions most relevant to the problem at hand. Our approach decouples guidance and planning, making it possible for basic planning algorithms to find desired paths earlier in the planning process. We demonstrate the efficacy of our approach in both robotics problems and applications in drug design. Our method is able to produce desirable paths quickly with no change to the underlying planner.
