多模态随机规划提升导航与多机器人协同的鲁棒性
A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination
- 基于交叉熵方法优化多模态策略，增强解空间探索能力
- 在陷阱环境和多机避障中成功率显著提升
- 支持分布式协同，避免死锁且无需集中式计算
本文提出一种滚动时域、基于采样的多模态规划方法，可在共同代价函数下优化多模态策略分布。通过交叉熵方法优化，该方法增强对局部极小值的鲁棒性，并促进解空间的有效探索。实验表明，该方法可自然扩展至多机器人无碰撞规划，使智能体共享多样化候选策略以避免死锁，团队可最小化全局目标，同时避免集中优化带来的计算复杂度。数值仿真显示，在陷阱环境与多机器人避障任务中，采用多模式策略显著提升成功率；硬件实验进一步验证了该方法的实时可行性与实际性能。
原文摘要 · Abstract (English)
In this paper, we present a receding-horizon, sampling-based planner capable of reasoning over multimodal policy distributions. By using the cross-entropy method to optimize a multimodal policy under a common cost function, our approach increases robustness against local minima and promotes effective exploration of the solution space. We show that our approach naturally extends to multi-robot collision-free planning, enables agents to share diverse candidate policies to avoid deadlocks, and allows teams to minimize a global objective without incurring the computational complexity of centralized optimization. Numerical simulations demonstrate that employing multiple modes significantly improves success rates in trap environments and in multi-robot collision avoidance. Hardware experiments further validate the approach's real-time feasibility and practical performance.
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。