发表机构
University of Delaware; Athena Research Center; National Technical University of Athens; HERON–Center of Excellence in Robotics（特拉华大学; 雅典娜研究中心; 雅典国家技术大学; 赫伦机器人卓越中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PathCover是基于点云的快速凸分解框架，采用RISP算法，可高效生成无障碍物多面体，速度较现有方法提升一个数量级，经仿真与实机验证适用于机器人导航。
AI 中文摘要
自主机器人导航需要快速生成无障碍物区域以用于轨迹规划，但现有走廊生成器难以满足实时、传感器速率的计算约束。为解决该瓶颈，我们提出PathCover框架，其由RISP驱动；RISP是一种新颖的随机算法，在温和的概率消除条件下，可从原始点云数据直接构造凸多面体，时间复杂度为期望线性。PathCover生成一系列重叠的无障碍物多面体，可安全约束下游的MPC（模型预测控制）和轨迹优化。我们从数学上保证该算法在有限步内终止，同时确保沿任意无障碍物参考路径的连续进展。在合成及真实世界LiDAR数据集上的大量基准测试表明，与现有最先进方法相比，PathCover实现了一个数量级的加速，同时保持了可比的走廊体积。完整流程通过高保真四旋翼仿真及四足机器人在受限环境中使用实时LiDAR感知导航的物理部署得到验证。
英文摘要
Autonomous robot navigation requires rapid construction of obstacle-points-separated convex regions for trajectory planning. When obstacles are represented as point clouds from LiDAR or depth cameras, these regions must be constructed directly from finite obstacle samples while providing suitable constraints for downstream optimization. However, existing corridor-generation methods often struggle to meet real-time, sensor-rate computational requirements. To address this bottleneck, we introduce RISP, a randomized algorithm that constructs convex polytopes from finite point-cloud data, and PathCover, which chains these polytopes along a reference path to form an overlapping corridor. We prove finite termination, sequential intersection, and complete path coverage with respect to the supplied finite point set. Under a probabilistic elimination condition, the sampling-and-elimination stage of RISP has expected O(n) time and unconditional worst-case O(n^2) time. Extensive benchmarks on synthetic and real-world LiDAR datasets demonstrate an order-of-magnitude speedup over state-of-the-art methods in both corridor generation as well as trajectory optimization, while closed-loop quadrotor simulation and a physical quadruped traversal demonstrate integration with downstream motion planners. These results establish corridor separation from the supplied point-cloud representation and practical implementation feasibility. Source code of the entire pipeline is available at https://github.com/kunalnk123690/PathCover.
Comments14 pages, 4 figures
