一种用于导航和多机器人协调的多模态随机规划方法
A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination
AI总结：
本文提出一种基于采样的滚动时域多模态随机规划器，通过交叉熵方法优化多模态策略，提升鲁棒性与探索能力，可扩展至多机器人无碰撞规划，经数值模拟和硬件实验验证其在陷阱环境、多机器人避障中的效果及实时性。
摘要：
本文提出了一种基于采样的滚动时域规划器，能够对多模态策略分布进行推理。通过使用交叉熵方法在共同成本函数下优化多模态策略，我们的方法提高了对局部最小值的鲁棒性，并促进了对解空间的有效探索。我们表明，该方法可自然扩展至多机器人无碰撞规划，使智能体能够共享多样化的候选策略以避免死锁，还能让团队在不产生集中式优化计算复杂度的情况下最小化全局目标。数值模拟表明，采用多模态可显著提高在陷阱环境和多机器人避障中的成功率。硬件实验进一步验证了该方法的实时可行性和实际性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
