Distributed Feedback Optimization for Multi-Robot Target Encirclement and Patrolling

Abstract

In this paper, we address multi-robot target monitoring and patrolling tasks via distributed feedback optimization. In particular, we design a distributed policy to steer a network of peer-to-peer robots toward a configuration minimizing a comprehensive index cost taking into account three different goals. First, the robots aim at disposing in a formation enclosing a given target. Second, each single robot aims at placing itself as close as possible to a specific point of interest. Third, the robots need to avoid dangerous locations modeled according to a suitable potential. To model this overall task, we resort to the formalism of aggregative feedback optimization, a recently emerged framework in which the goal is to minimize the sum of local functions each depending on both local (e.g., the position of a robot) and global variables (e.g., the barycenter of a team of robots) while concurrently taking into account also the robots’ nonlinear dynamics. We test our distributed strategy via realistic Webots simulations of a team of Crazyflie nano-quadrotors in a ROS 2 framework.