A Meta-Heuristic Approach For Energy-Efficient Multi-Agent Path Planning

We address the problem of multi-agent path planning (MAPP) by taking into account the total energy consumption of all agents, as measured by their cumulative distance travelled. A deterministic meta-heuristic technique called guided iterative prioritized planning (GIPP) is proposed to find good solutions with respect to this cumulative cost measure in a crowded environment. GIPP progressively explores a larger search space while incorporating a local optimum detection and resolution scheme to prevent the search process from being trapped in an unfeasible locally optimal solution. Comparative evaluations show that GIPP strikes a good tradeoff between the solution quality and computational time.