Dynamic Multi-Target Assignment with Decentralised Online Learning to Achieve Multiple Synchronised Goals

Abstract

In this paper, we present a decentralised online decision-making strategy for multi-agents carrying out a cooperative mission. Our solution provides the capability for agents to dynamically choose their best targets and arrive at their target locations simultaneously at pre-specified angles. Additionally, the agents are able to cope with any obstacles encountered without compromising the mission goals. The algorithm combines game-theoretic regret minimisation with current best-practice solutions to satisfy complex mission requirements. It is decentralised and readily scalable to a large number of agents for wide area operations. Simulation results show it can be applied to teams of agents in challenging environments and exhibits fast convergence and adaptability.