Continuously Varying Formation for Heterogeneous Multi-Agent Systems With Novel Potential Field Avoidance

Abstract

This article presents a novel approach to time-varying formation for heterogeneous multiagent systems (MASs), and uses a novel artificial potential field (APF) algorithm for collision and obstacle avoidance. For a team of agents, a set of formations are designed for the use case, and based on the circumstances for the system, the formation can be adjusted over a continuous spectrum of possible formations. This is done as a means of minimizing the amount of changing required in order for the formation to maneuver through an unknown environment. For obstacle avoidance, a modification to classical potential fields is implemented which utilizes the agent's heading, velocity, and other parameters to provide a better optimized avoidance algorithm. Terminal sliding mode controllers are applied for the control of the individual agents in the team. These are validated in both simulations and experiments for a team of quadrotor and mobile robots.