Coordinated Motion Planning for Multi-Arm Systems via Iterative LQ Games
Robotics
Summary
The authors tackle the problem of planning safe and smooth movements for multiple robot arms working together in the same space. They treat each robot arm as a player in a game, each trying to reach its goal while avoiding collisions with others. Their method uses a game-theoretic approach with math tools that help predict the best moves based on the other arms’ positions, leading to coordinated paths. Experiments show that this approach works better than traditional planners, especially for robots with many joints.
Authors
Junyoung Kim, Hanwen Ren, Lei Zhang, Ahmed H. Qureshi
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.