Heuristics pool for hyper-heuristic selection during task allocation in a heterogeneous swarm of marine robots

Abstract

For the purpose of enabling long-term autonomy of a heterogeneous swarm of marine robots, task allocation and sequencing are introduced into the system's energy management procedures. In a scenario where the system needs to autonomously go about its monitoring mission and survive long- term, the available maximum capacity of 5 USVs - aPad platforms which represent the charging hubs of the system - is usually outnumbered by the number of active charging requests, leading to a need for careful planning and optimisation of robot activities. A two-layered system of decision-making algorithms is developed: a low-level specific solution-focused set of algorithms based on various machine learning paradigms, and a high-level hyper-heuristic which selects between them. This paper focuses on the lower level of this decision-making system, and details some of the approaches to task sequencing to be offered for selection, primarily based on differential evolution and k-means clustering, along with factoring in the effects of water currents and wind. Achieved simulation results are discussed and some directions for further work are suggested.