发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出迭代LQ博弈框架用于多机械臂协调运动规划，通过可微碰撞惩罚生成安全高效轨迹，性能优于传统方法，凸显微分博弈在多机器人操作中的有效性。
AI 中文摘要
在共享工作空间中，高自由度机器人操作器的多智能体运动规划仍是一个基础但极具挑战性的问题。集中式规划器通常存在可扩展性差的问题，而分散式方法则面临鲁棒性和安全性方面的担忧。博弈论公式化是建模智能体交互的一种有前景的方法，有可能克服这些局限性。然而，其在铰接式多机械臂系统中的应用仍然有限。本文提出了一种用于多机械臂运动规划的迭代线性二次（LQ）博弈框架，其中每个机械臂被建模为独立智能体，在基于共享全局状态和碰撞约束与其他智能体交互的同时优化自身目标。该方法通过在名义轨迹附近线性化动力学并近似代价来求解一系列局部LQ博弈，利用Riccati反向递推得到反馈纳什策略。为解决铰接式系统的挑战，我们将自碰撞和臂间碰撞的可微惩罚项纳入优化流程，从而生成协调、感知碰撞的轨迹。实验表明，我们的框架在高维场景中能生成平滑、安全且高效的轨迹，优于传统方法，凸显了微分博弈公式化在多机器人操作中的有效性。
英文摘要
Multi-agent motion planning for high-degree-of-freedom robotics manipulators in shared workspaces remains a fundamental yet challenging problem. Centralized planners often suffer from poor scalability, while decentralized approaches face robustness and safety concerns. Game-theoretic formulations offer a promising approach for modeling agent interactions, potentially overcoming these limitations. However, their application to articulated multi-arm systems remains limited. This paper presents an iterative Linear Quadratic (LQ) game framework for multi-manipulator motion planning, where each manipulator is modeled as an independent agent optimizing its own objective while interacting with other agents based on shared global states and collision constraints. The method solves a series of local LQ games by linearizing the dynamics and approximating the cost around a nominal trajectory, with Riccati backward recursions yielding feedback Nash strategies. To address the challenges of articulated systems, we incorporate differentiable penalties for self-collision and inter-arm collision into the optimization pipeline, enabling coordinated, collision-aware trajectory generation. Experiments demonstrate that our framework produces smooth, safe, and efficient trajectories in high-dimensional settings, outperforming traditional methods. This highlights the effectiveness of differential game formulations for multi-robot manipulation.
