Density Control for Decentralized Autonomous Agents with Conflict Avoidance

Abstract This paper describes a method to control the density distribution of a large number of autonomous agents. Our approach leverages from the fact that there are a large number of agents in the system, and hence the time evolution of the probabilistic density distribution of agents can be described as a Markov chain. Once this description is obtained, a Markov chain matrix is synthesized to drive the multi-agent system density to a desired steady-state density distribution, in a probabilistic sense, while satisfying some motion and conflict avoidance constraints. Later, we introduce an adaptive density control method based on real time density feedback to synthesize a time-varying Markov matrix, which leads to better convergence to the desired density distribution. This paper also introduces a decentralized density computation method, which guarantees that all agents will have a best, and common, density estimate in a finite, with an explicit bound, number of communication updates.