发表机构
Technical University of Munich; University of California at Riverside（慕尼黑工业大学; 加州大学河滨分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多机器人控制中计算延迟与查询点差异问题，提出异步协同在线学习策略与伴随MAS分布式控制律，经无人水面艇仿真验证，其学习与控制性能优于现有最优方法。
AI 中文摘要
确保不确定环境下多智能体系统（MASs）的安全运行对协同机器人至关重要，外部干扰和不准确的动态模型会严重损害性能与可靠性。为应对这一挑战，校准后的机器学习模型（尤其是高斯过程（GP）回归）因具备可解释的性能量化能力而被广泛采用。由于MASs的互联通信支持协同学习，智能体可通过与邻居交换局部GP推断，并通过分布式GP策略聚合接收的信息来提升学习性能。然而，智能体间计算能力和预测任务的差异不可避免地导致异构计算延迟和查询点差异，现有聚合方法往往忽略这些因素。为克服这些局限，本研究提出一种异步协同学习策略，该策略明确考虑了预测精度、查询点变化和延迟效应；此外，还开发了一种基于伴随MAS的分布式控制律，以确保所需的控制性能。对无人水面艇的仿真验证了所提方法的有效性，与现有最优方法相比，其在学习和控制性能上均实现了显著提升。
英文摘要
Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances and inaccurate dynamic models can significantly compromise performance and reliability. To address this challenge, calibrated machine learning models, particularly Gaussian process (GP) regression, are extensively employed due to their interpretable performance quantification. As the interconnected communication of MASs facilitates cooperative learning, agents are able to enhance learning performance by exchanging local GP inferences with their neighbors and aggregating the received information via distributed GP strategies. However, variations in computational power and prediction tasks among agents inevitably lead to heterogeneous computational delays and differences in query points, which are often overlooked in existing aggregation methods. To overcome these limitations, this work proposes an asynchronous cooperative learning strategy that explicitly accounts for prediction accuracy, query point variations and delay effects. Additionally, a distributed control law based on an adjoint MAS is developed to ensure the desired control performance. Simulations on unmanned surface vehicles validate the effectiveness of the proposed approach, demonstrating substantial improvements in both learning and control performance compared to the state-of-the-art approaches.
