Coordinated Motion Planning for Multi-Arm Systems via Iterative LQ Games

2026-08-27

AI Summary

该论文针对多机械臂系统的协调运动规划问题，提出了一种基于迭代线性二次型游戏的方法，通过引入可微分的碰撞惩罚项实现高效、安全的轨迹生成。

Abstract

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.

Problem

• multi-agent motion planning
• high-degree-of-freedom robotics manipulators
• shared workspaces
• centralized planners
• decentralized approaches

Innovation

• Iterative LQ Games
• Multi-manipulator Motion Planning
• Differentiable Penalties
• Collision-aware Trajectory Generation
• Feedback Nash Strategies