Decentralized Control of Autonomous Vehicles

Abstract

Decentralized control methods are appealing in coordination ofmultiple vehicles due to their low demand for long-range communication and their robustness to single-point failures. An important approach in decentralized multi-vehicle control involves artificial potentials or digital pheromones. In this paper we explore a decentralized approach to path generation for a group of combat vehicles in a battlefield scenario. The mission is to maneuver the vehicles to cover a target area. The vehicles are required to maintain good overall area coverage, and avoid obstacles and threats during the maneuvering. The gradient descent method is used, where each vehicle makes its moving decision by minimizing a potential function that encodes information about its neighbours, obstacles, threats and the target. We conduct analysis of vehicle behaviors by studying the vector field induced by the potential function. Simulation has shown that this approach leads to interesting emergent behaviors, and the behaviors can be varied by adjusting the weighting coefficientsof different potential function terms.