SwarmPRM：面向大规模集群机器人系统的概率路线图运动规划
SwarmPRM: Probabilistic Roadmap Motion Planning for Large-Scale Swarm Robotic Systems
AI总结：
本文提出SwarmPRM，一种分层可扩展的基于采样的运动规划方法，利用GMM表示集群宏观状态并构建高斯路线图，结合CVaR增强风险意识，通过线性规划优化轨迹，显著提升大规模集群机器人的计算效率、可扩展性与轨迹质量。
摘要：
由众多协作智能体组成的大规模集群机器人系统在各个领域执行自主任务方面展现出巨大潜力。然而，由于机器人联合状态空间的指数级增长，传统运动规划方法常在可扩展性与解质量之间面临权衡。为此，本文提出SwarmPRM，一种面向大规模集群机器人的分层、可扩展、计算高效且具备风险意识的基于采样的运动规划方法。SwarmPRM利用高斯混合模型（GMM）表示集群的宏观状态，并在高斯空间中构建概率路线图（称为高斯路线图），以生成GMM的传输轨迹。在微观阶段，每个机器人跟随该轨迹。为增强轨迹安全性，SwarmPRM在碰撞检测过程中引入条件风险价值（CVaR），赋予所构建高斯路线图风险意识特性。随后，SwarmPRM设计线性规划公式，计算该路线图内的最优GMM传输轨迹。大量仿真表明，SwarmPRM在计算效率、可扩展性和轨迹质量上优于现有方法，并具备调整生成轨迹风险容忍度的能力。
英文摘要：
Large-scale swarm robotic systems consisting of numerous cooperative agents show considerable promise for performing autonomous tasks across various sectors. Nonetheless, traditional motion planning approaches often face a trade-off between scalability and solution quality due to the exponential growth of the joint state space of robots. In response, this work proposes SwarmPRM, a hierarchical, scalable, computationally efficient, and risk-aware sampling-based motion planning approach for large-scale swarm robots. SwarmPRM utilizes a Gaussian Mixture Model (GMM) to represent the swarm's macroscopic state and constructs a Probabilistic Roadmap in Gaussian space, referred to as the Gaussian roadmap, to generate a transport trajectory of GMM. This trajectory is then followed by each robot at the microscopic stage. To enhance trajectory safety, SwarmPRM incorporates the conditional value-at-risk (CVaR) in the collision checking process to impart the property of risk awareness to the constructed Gaussian roadmap. SwarmPRM then crafts a linear programming formulation to compute the optimal GMM transport trajectory within this roadmap. Extensive simulations demonstrate that SwarmPRM outperforms state-of-the-art methods in computational efficiency, scalability, and trajectory quality while offering the capability to adjust the risk tolerance of generated trajectories.
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。
