Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges

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…

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 …

通过自适应采样器实现基于采样规划的估计知情任意时间搜索

Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler

本文提出 MIT* 规划器，通过估计知情集、自适应采样和稀疏碰撞检测引导的惰性反向搜索，提升高维路径规划的初始收敛速度、计算效率与成功率。

使用全连接信息树（FCIT*）的无最近邻渐近最优运动规划

Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)

本文提出全连接信息树（FCIT*）算法，利用SIMD并行性降低边评估成本并消除最近邻结构，在MotionBenchMaker数据集上比现有ASAO和满足型算法更快找到初始解并渐近收敛到最优。

面向部分未知环境的机器人覆盖路径随时重规划

Anytime Replanning of Robot Coverage Paths for Partially Unknown Environments

本文提出OARP-Replan随时覆盖重规划方法，通过求解整数线性规划的线性松弛，在给定时间预算内对被中断的覆盖路径进行近最优重规划，以应对部分未知环境中的静态障碍物，并在仿真和工业级自主机器人实验中验证了其优于贪心绕行重规划及其他先进覆盖规划器的性能。

A-ePA*SE：面向慢评估的任意时间并行边缘A*算法

A-ePA*SE: Anytime Edge-Based Parallel A* for Slow Evaluations

本文提出A-ePA*SE，将任意时间特性引入并行边缘评估的ePA*SE算法，以在时间预算内快速提供并改进可行解，实验证明其效率显著优于其他任意时间搜索方法。