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Sensors 2017, 17(4), 762; doi:10.3390/s17040762

Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics

1
TECNALIA, 48160 Derio, Bizkaia, Spain
2
Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Bizkaia, Spain
3
Basque Center for Applied Mathematics, 48009 Bilbao, Bizkaia, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: José-Fernán Martínez
Received: 23 December 2016 / Revised: 10 March 2017 / Accepted: 23 March 2017 / Published: 4 April 2017
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Abstract

Robotics deployed in the underwater medium are subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this context the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by devising three heuristic solvers aimed at efficiently scheduling tasks in robotic swarms, which collaborate together to accomplish a mission, and by presenting experimental results obtained over realistic scenarios in the underwater environment. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks and the relative execution order of such tasks within the schedule of certain robots. The obtained results reveal interesting differences in terms of Pareto optimality and spread between the algorithms considered in the benchmark, which are insightful for the selection of a proper task scheduler in real underwater campaigns. View Full-Text
Keywords: scheduling; heuristic; multi-objective optimization; random keys encoding; underwater robots; Harmony Search scheduling; heuristic; multi-objective optimization; random keys encoding; underwater robots; Harmony Search
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MDPI and ACS Style

Landa-Torres, I.; Manjarres, D.; Bilbao, S.; Del Ser, J. Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics. Sensors 2017, 17, 762.

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