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This paper proposes a distributed energy management task allocation strategy for a multi-robot system (MRS) involved in goods transportation. The main goal in such type of application is maximizing the productivity by transporting the maximum of goods. However, an important aspect of the optimal use of an MRS is an efficient energy management. The aim of this work is to develop a distributed cooperative strategy that considers energy management in task allocation. For this purpose, an Iterative Local Search-based (ILS) metaheuristic and a novel heuristic-based utility function are developed to solve the multi-robot task allocation problem. The proposed strategy uses the MRS as a population of individuals to solve the task allocation in a parallel distributed manner. Each robot runs the ILS-based algorithm and provides its solution for the task allocation, due to the fact that some of ILS parameters are randomly chosen. Thus, this strategy combines the exploration advantage of the population-based metaheuristics and the exploitation advantage and easy implementation on embedded systems (e.g., MRS) of the single-point-search metaheuristics. The proposed strategy is evaluated in a simulated environment and compared to three heuristics, in terms of the average consumed energy, the productivity of the system, and productivity per energy unit consumed.

Energy-aware task allocation strategy for multi robot system