Research on Multi-Objective Multi-Robot Task Allocation by Lin–Kernighan–Helsgaun Guided Evolutionary Algorithms

Zhenqiang Zhang, Sile Ma and Xiangyuan Jiang ()
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Zhenqiang Zhang: Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China
Sile Ma: Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China
Xiangyuan Jiang: Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China

Mathematics, 2022, vol. 10, issue 24, 1-17

Abstract: Multi-robot task allocation (MRTA) and route planning are crucial for a large-scale multi-robot system. In this paper, the problem is formulated to minimize the total energy consumption and overall task completion time simultaneously, with some constraints taken into consideration. To represent a solution, a novel one-chromosome representation technique is proposed, which eases the consequent genetic operations and the construction of the cost matrix. Lin–Kernighan–Helsgaun (LKH), a highly efficient sub-tour planner, is employed to generate prophet generation beforehand as well as guide the evolutionary direction during the proceeding of multi-objective evolutionary algorithms, aiming to promote convergence of the Pareto front. Numerical experiments on the benchmark show the LKH guidance mechanism is effective for two famous multi-objective evolutionary algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm (NSGA), of which LKH-guided NSGA exhibits the best performance on three predefined indicators, namely C-metric, HV, and Spacing, respectively. The generalization experiment on a multiple depots MRTA problem with constraints further demonstrates the effectiveness of the proposed approach for practical decision making.

Keywords: multi-robot task allocation; route planning; multi-objective evolutionary algorithm; Lin–Kernighan–Helsgaun; non-dominated sorting genetic algorithm; Pareto front; decision making (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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