Improved Soft Duplicate Detection in Search-Based Motion Planning
IEEE International Conference on Robotics and Automation(2022)
摘要
Search-based techniques have shown great success in motion planning problems such as robotic navigation by discretizing the state space and precomputing motion primitives. However in domains with complex dynamic constraints, constructing motion primitives in a discretized state space is non-trivial. This requires operating in continuous space which can be challenging for search-based planners as they can get stuck in local minima regions. Previous work [1] on planning in continuous spaces introduced soft duplicate detection which requires search to compute the duplicity of a state with respect to previously seen states to avoid exploring states that are likely to be duplicates, especially in local minima regions. They propose a simple metric utilizing the Euclidean distance between states, and proximity to obstacles to compute the duplicity. In this paper, we improve upon this metric by introducing a kinodynamically informed metric, subtree overlap, between two states as the similarity between their successors that can be reached within a fixed time horizon using kinodynamic motion primitives. This captures the intuition that, due to robot dynamics, duplicate states can be far in Euclidean distance and result in very similar successor states, while non-duplicate states can be close and result in widely different successors. Our approach computes the new metric offline for a given robot dynamics, and stores the subtree overlap value for all possible relative state configurations. During search, the planner uses these precomputed values to speed up duplicity computation, and achieves fast planning times in continuous spaces in addition to completeness and sub-optimality guarantees. Empirically, we show that our improved metric for soft duplicity detection in search-based planning outperforms previous approaches in terms of planning time, by a factor of 1.5 to 2× on 3D and 5D planning domains with highly constrained dynamics.
更多查看译文
关键词
soft duplicate detection,search-based motion planning,search-based techniques,motion planning problems,robotic navigation,precomputing motion primitives,complex dynamic constraints,discretized state space,continuous space,search-based planners,local minima regions,duplicates,simple metric utilizing,Euclidean distance,kinodynamically informed metric overlap,subtree overlap,kinodynamic motion primitives,duplicate states,similar successor states,nonduplicate states,given robot dynamics,possible relative state configurations,duplicity computation,soft duplicity detection,search-based planning,planning time
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要