多机器人协同控制中考虑定位不确定性的分布式鲁棒算法
Distributed Linear Quadratic Gaussian for Multi-Robot Coordination with Localization Uncertainty
- 基于随机LQG框架，融合定位不确定性建模
- 保证多机网络在不确定环境下的稳定收敛
- 适合需高精度协同的无人机/自动驾驶场景
本文针对存在定位不确定性时的多机器人系统（MRS）分布式协同控制问题，提出一种随机线性二次高斯（LQG）控制策略。该方法在优化性能指标的同时，确保移动机器人的协调性，并显式建模定位测量中的固有不确定性，从而实现鲁棒决策与协同。我们分析了所提控制协议下的系统稳定性，推导出多机器人网络收敛的条件。通过Robotrium仿真平台的实验验证了该方法的有效性，展示了其在存在定位不确定性的真实场景中的实际应用潜力。
原文摘要 · Abstract (English)
This paper addresses the problem of distributed coordination control for multi-robot systems (MRSs) in the presence of localization uncertainty using a Linear Quadratic Gaussian (LQG) approach. We introduce a stochastic LQG control strategy that ensures the coordination of mobile robots while optimizing a performance criterion. The proposed control framework accounts for the inherent uncertainty in localization measurements, enabling robust decision-making and coordination. We analyze the stability of the system under the proposed control protocol, deriving conditions for the convergence of the multi-robot network. The effectiveness of the proposed approach is demonstrated through experimental validation using Robotrium simulation experiments, showcasing the practical applicability of the control strategy in real-world scenarios with localization uncertainty.
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