论文标题
通过强大的控制屏障功能与学习的不确定性通过强大的控制屏障功能进行安全的多代理互动
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties
论文作者
论文摘要
在现实世界中运行的机器人必须在与许多异质因素和障碍物互动的同时导航并保持安全性。多代理控制障碍功能（CBF）已成为一种计算有效的工具，可确保在多机构环境中的安全性，但它们对机器人动力学和其他代理的动力学都具有完美的了解。尽管对机器人动态的了解可能是相当众所周知的，但在现实环境中代理的异质性意味着我们对其他代理动力学的预测总是存在很大的不确定性。这项工作旨在使用矩阵变量高斯流程模型来学习这些动态不确定性的高信心界限，并将它们纳入强大的多代理CBF框架中。我们将所得的最小鲁棒CBF转换为二次程序，可以实时有效地解决。我们通过仿真结果验证，标称多代理CBF经常在代理相互作用期间违反，而我们的稳健配方则保持安全性，并以更高的概率和适应学习的不确定性。
Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties
