Parameter Identification for Multirobot Systems Using Optimization-based Controllers

Abstract

This paper considers the problem of parameter identification for a multirobot system. We wish to understand how can an adversarial observer reverse-engineer the tasks being performed by a team of robots by simply observing their positions. To address this question, we use the concept of persistency of excitation from system identification. Each robot in the team uses optimization-based controllers for mediating between task satisfaction and collision avoidance. These controllers exhibit an implicit dependence on the task's parameters which poses a hurdle for deriving necessary conditions for parameter identification, since such conditions usually require an explicit relation. We address this bottleneck by using duality theory and SVD of active collision avoidance constraints and derive an explicit relation between each robot's task parameters and its control inputs. This allows us to derive the main necessary conditions for successful identification which agree with our intuition. We demonstrate the importance of these conditions through numerical simulations by using (a) an adaptive observer and (b) an unscented Kalman filter for goal and gain estimation in various geometric settings. These simulations show that under circumstances where parameter inference is supposed to be infeasible per our conditions, both these estimators fail, and likewise when it is feasible, both converge to the true parameters. Videos of these results are available at https://tinyurl.com/6v6bmk86