多机器人在不确定下安全交互，用随机约束优化轨迹
Chance-constrained Linear Quadratic Gaussian Games for Multi-robot Interaction under Uncertainty
- 构建带随机约束的多机器人博弈模型，可处理系统不确定性
- 算法收敛到反馈广义纳什均衡，保证安全且轨迹更优
- 适用于自动驾驶、协作机器人等需安全避障的场景
我们研究了不确定环境下多机器人交互的安全性问题。为此，提出一种带有耦合约束和系统不确定性的随机约束线性二次高斯博弈模型。通过可计算的重构方法，并设计对偶上升算法求解。理论证明该算法收敛至重构博弈的反馈广义纳什均衡，确保随机约束被满足。在驾驶仿真与真实机器人实验中验证了方法的有效性：在不确定性条件下保持安全，生成的轨迹比单机模型预测控制更不保守。
原文摘要 · Abstract (English)
We address safe multi-robot interaction under uncertainty. In particular, we formulate a chance-constrained linear quadratic Gaussian game with coupling constraints and system uncertainties. We find a tractable reformulation of the game and propose a dual ascent algorithm. We prove that the algorithm converges to a feedback generalized Nash equilibrium of the reformulated game, ensuring the satisfaction of the chance constraints. We test our method in driving simulations and real-world robot experiments. Our method ensures safety under uncertainty and generates less conservative trajectories than single-agent model predictive control.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。