论文标题
使用二元性（扩展版）的基于可行区域的可行识别
Feasible Region-based Identification Using Duality (Extended Version)
论文作者
论文摘要
我们考虑了代表多机器人系统中各个机器人执行的任务的参数估算界限的问题。在我们以前的工作中，我们基于激发分析的持久性来得出必要的条件，以确切地识别这些参数。我们得出的结论是，根据机器人的任务，单个机器人的动态可能无法满足这些条件，从而防止了精确的推断。作为该工作的扩展，本文着重于在不满足此类条件时估算任务参数的界限。团队中的每个机器人都使用基于优化的控制器来介导任务满意度和避免碰撞。我们使用此优化的KKT条件和主动碰撞避免约束的SVD来得出Lagrange乘数，机器人动力学和任务参数之间的明确关系。使用这些关系，我们能够在每个机器人的任务参数上得出界限。通过数值模拟，我们展示了我们提出的基于区域的识别方法如何在常规估计器（例如UKF）失败时为参数产生可行区域。此外，经验证据表明，这种方法产生的合同集融合到真实参数的速度要比基于UKF的估计值收敛的速度要快得多。这些结果的视频可从https://bit.ly/2jdmgej获得
We consider the problem of estimating bounds on parameters representing tasks being performed by individual robots in a multirobot system. In our previous work, we derived necessary conditions based on persistency of excitation analysis for the exact identification of these parameters. We concluded that depending on the robot's task, the dynamics of individual robots may fail to satisfy these conditions, thereby preventing exact inference. As an extension to that work, this paper focuses on estimating bounds on task parameters when such conditions are not satisfied. Each robot in the team uses optimization-based controllers for mediating between task satisfaction and collision avoidance. We use KKT conditions of this optimization and SVD of active collision avoidance constraints to derive explicit relations between Lagrange multipliers, robot dynamics, and task parameters. Using these relations, we are able to derive bounds on each robot's task parameters. Through numerical simulations, we show how our proposed region based identification approach generates feasible regions for parameters when a conventional estimator such as a UKF fails. Additionally, empirical evidence shows that this approach generates contracting sets which converge to the true parameters much faster than the rate at which a UKF based estimate converges. Videos of these results are available at https://bit.ly/2JDMgeJ
