Supervisory Control for Cost-Effective Redistribution of Robotic Swarms

Abstract

Dynamic assignment and re-assignment of large number of simple and cheap robots across multiple sites is relevant to applications like autonomous survey, environmen- tal monitoring and reconnaissance. In this paper, we present supervisory control laws for cost-effective (re)-distribution of a robotic swarm among multiple sites. We consider a robotic swarm consisting of tens to hundreds of simple robots with limited battery life and limited computation and communication capabilities. The robots have the capability to recognize the site that they are in and receive messages from a central supervisory controller, but they cannot communicate with other robots. There is a cost (e.g., energy, time) for the robots to move from one site to another. These limitations make the swarm hard to control to achieve the desired configurations. Our goal is to design control laws to move the robots from one site to another such that the overall cost of redistribution is minimized. This problem can be posed as an optimal control problem (which is hard to solve optimally), and has been studied to a limited extent in the literature when the cost objective is time. We consider the total energy consumed as the cost objective and present a linear programming based heuristic for computing a stochastic transition law for the robots to move between sites. We evaluate our method for different objectives and show through Monte Carlo simulations that our method outperforms other proposed methods in the literature for the objective of time as well as more general objectives (like total energy consumed).