多机器人协同定位下，安全规划运动轨迹并提升效率。
Extended Version: Multi-Robot Motion Planning with Cooperative Localization
- 将协同定位纳入概率约束规划，显式处理机器人间相关性。
- 采样规划器扩展后仍保持概率完备性，且偏差策略显著提升性能。
- 适合需要高安全性与协作感知的多机系统场景。
我们研究在运动和测量噪声下的不确定多机器人运动规划问题（CL-MRMP），其中每个机器人可作为邻近队友的传感器。将CL-MRMP形式化为机会约束规划问题，提出一种保障安全的算法，显式考虑机器人间的相关性。该方法扩展了基于采样的规划器，在保持概率完备性的前提下求解CL-MRMP。为进一步提升效率，引入新颖的偏差策略。在多个基准测试中验证了该方法的有效性，偏差策略带来显著性能提升。
原文摘要 · Abstract (English)
We consider the uncertain multi-robot motion planning (MRMP) problem with cooperative localization (CL-MRMP), under both motion and measurement noise, where each robot can act as a sensor for its nearby teammates. We formalize CL-MRMP as a chance-constrained motion planning problem, and propose a safety-guaranteed algorithm that explicitly accounts for robot-robot correlations. Our approach extends a sampling-based planner to solve CL-MRMP while preserving probabilistic completeness. To improve efficiency, we introduce novel biasing techniques. We evaluate our method across diverse benchmarks, demonstrating its effectiveness in generating motion plans, with significant performance gains from biasing strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。