Fast Planning Over Roadmaps via Selective Densification

Densification Strategies for Anytime Motion Planning over Large Dense Roadmaps

We consider the problem of computing shortest paths in a dense motion-planning roadmap $\mathcal{G}$. We assume that~$n$, the number of vertices of $\mathcal{G}$, is very large. Thus, using any path-planning algorithm that directly searches $\mathcal{G}$, running in $O(V\textrm{log}V + E) \approx O(n^2)$ time, becomes unacceptably expensive. We are therefore interested in anytime search to obtain successively shorter feasible paths and converge to the shortest path in $\mathcal{G}$. Our key insight is to provide existing path-planning algorithms with a sequence of increasingly dense subgraphs …

面向高维窄通道环境的快速运动规划选择性密集化方法

Selective Densification for Rapid Motion Planning in High Dimensions with Narrow Passages

本文提出一种基于多分辨率采样与选择性密集化的高效运动规划框架及其双向版本，通过稀疏与密集样本的无缝切换，在保持规划速度和完备性的同时有效应对高维窄通道环境，并在多种配置空间和真实机器人实验中优于现有最先进方法。

Anytime Motion Planning on Large Dense Roadmaps with Expensive Edge Evaluations

We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are computationally expensive. A large dense roadmap (graph) can typically ensure the existence of high quality solutions for most motion-planning problems, but the size of the roadmap, particularly in high-dimensional spaces, makes existing search-based planning algorithms computationally expensive. We deal with the challenges of expensive search and collision checking in two ways. First, we frame the problem of anytime m…

基于场景图的采样运动规划在感知约束下的应用

Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints

本文提出MOPS-PRM，一种基于场景图的运动规划方法，通过整合多物体感知成本以提升高自由度机器人在复杂环境中的任务执行效率。