Multi-objective Robust Simultaneous Arrival Matching for Aerial Swarms

Abstract

In this paper, we present an efficient approach to solving the simultaneous arrival matching problem for aerial swarm robots under complex constraints. By leveraging Pontryagin’sminimum principle, we derive the assignment cost for any robot-target pair, which takes into account the motion planning for the simultaneous arrival of each aerial robot. Then we explicitly formulate the simultaneous arrival matching problem as a multi-objective optimization problem and present a multi-objective evolutionary algorithm based on decomposition(MOEA/D) with problem-specific improvement for simultaneous arrival matching (SAM),called MOEA/D-SAM. Simulation results show that the MOEA/D-SAM outperforms significantly general-purpose MOEA/D, and an off-the-shelf multi-objective algorithm, i.e., NSGA-II. The advantages of the MOEA/D-SAM over the single-objective optimization algorithm are also shown.