Parameter Identification for Multirobot Systems Using Optimization Based Controllers (Extended Version)

Abstract

This paper considers the problem of parameter identification for a multirobot system. We wish to understand when is it feasible for an adversarial observer to reverse-engineer the parameters of tasks being performed by a team of robots by simply observing their positions. We address this question by using 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 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://bit.ly/3kQYj5J

Noncooperative Herding With Control Barrier Functions: Theory and Experiments

“…This is not a stringent assumption because if the dynamics are unknown, the dog robots can learn the dynamics online using multiagent system identification algorithms, some of which we have developed in our prior work [19], [20] and use certainty equivalence to design the controllers. We can pose the dog robots' problem as follows:…”

The Before, During, and After of Multi-robot Deadlock

“…Multi-robot systems have been studied extensively for solving a variety of complex tasks such as target search (Kantor et al, 2003;Grover et al, 2020b), sensor coverage (Cortes et al, 2004), environmental exploration (Burgard et al, 2005) and perimeter guarding (Feng and Yu, 2019). Global coordinated behaviors result from executing local control laws on individual robots interacting with their neighbors (Ogren et al, 2002;Olfati-Saber et al, 2007).…”

The Before, During, and After of Multi-Robot Deadlock

“…Multirobot systems have been studied extensively for solving a variety of complex tasks such as target search Kantor et al [2003], Grover et al [2020b], sensor coverage Cortes et al [2004], environmental exploration Burgard et al [2005] and perimeter guarding Feng and Yu [2019]. Global coordinated behaviors result from executing local control laws on individual robots interacting with their neighbors Ogren et al [2002], Olfati-Saber et al [2007].…”