Decentralized Swarm Control in Communication-Constrained Environments Using a Blended Leader Follower-Artificial Potential Field With Biologically Inspired Interactions

This paper presents a study of how communication ranges influence the performance of a new decentralized control method for swarms of autonomously navigating ground vehicles that uses a blended leader–follower/artificial potential field approach. While teams of autonomous ground vehicles (AGVs) that can navigate autonomously through off-road terrain have a variety of potential uses, it may be difficult to control the team in low-infrastructure environments that lack long-range radio communications capabilities. In this work, we propose a novel decentralized swarm control algorithm that combines the potential field planning method with the leader–follower (LF) control algorithm and biologically inspired inter-robot interactions to effectively control the navigation of a team of AGV (swarm) through rough terrain using only a single lead vehicle. We use simulated experimentation to demonstrate the robustness of this approach using only point-to-point wireless communication with realistic communication ranges. Furthermore, we analyze the range requirements of the communication network as the number in the swarm increases. We find that wireless communication range must increase as the number of agents in the swarm increases in order to effectively control the swarm. Our analysis showed that mission success decreased by 40% when the communication range was reduced from 100 m to 200 m, with the exact reduction also depending on the number of vehicles.