使用对偶性的可行的基于区域的识别（扩展版本）
Feasible Region-based Identification Using Duality (Extended Version)
摘要：
我们考虑的问题，估计界限的参数表示的任务由个人机器人在多机器人系统。在我们以前的工作中，我们推导出必要条件的基础上的持久性的激发分析，这些参数的精确识别。我们的结论是，根据机器人的任务，个别机器人的动态可能无法满足这些条件，从而防止精确的推断。作为这项工作的延伸，本文侧重于估计界限的任务参数时，这些条件不满足。团队中的每个机器人都使用基于优化的控制器来协调任务满意度和避免碰撞。我们使用KKT条件的这种优化和SVD的主动防撞约束，推导出显式的拉格朗日乘子，机器人动力学和任务参数之间的关系。使用这些关系，我们能够推导出每个机器人的任务参数的界限。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传统估计器（如UKF）失败时生成参数的可行区域。此外，经验证据表明，这种方法产生的收缩集收敛到真实的参数比UKF估计收敛的速度快得多。这些结果的视频可在https://bit.ly/2JDMgeJ上获得
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
