GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications

The use of multiagent systems (MASs) in real-world applications keeps increasing, and diffuses into new domains, thanks to technological advances, increased acceptance, and demanding productivity requirements. Being able to automate the generation of mission plans for MASs is critical for managing c...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 52; H. 10; S. 10627 - 10638
Hauptverfasser: Miloradovic, Branko, Curuklu, Baran, Ekstrom, Mikael, Papadopoulos, Alessandro Vittorio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Automation
Extended colored traveling salesperson problem (ECTSP)
Extended Colored Traveling Salesperson Problem (ECTSP)Genetic Algorithm (GA)Multirobot Mission PlanningMultirobot Systems
genetic algorithm (GA)
Genetics
Integer programming
Linear programming
Mixed integer
Multiagent systems
Multiple robots
multirobot mission planning
multirobot systems
Optimization
Planning
Robots
Task analysis
Taxonomy
Urban areas
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:The use of multiagent systems (MASs) in real-world applications keeps increasing, and diffuses into new domains, thanks to technological advances, increased acceptance, and demanding productivity requirements. Being able to automate the generation of mission plans for MASs is critical for managing complex missions in realistic settings. In addition, finding the right level of abstraction to represent any generic MAS mission is important for being able to provide general solution to the automated planning problem. In this article, we show how a mission for heterogeneous MASs can be cast as an extension of the traveling salesperson problem (TSP), and we propose a mixed-integer linear programming formulation. In order to solve this problem, a genetic mission planner (GMP), with a local plan refinement algorithm, is proposed. In addition, the comparative evaluation of CPLEX and GMP is presented in terms of timing and optimality of the obtained solutions. The algorithms are benchmarked on a proposed set of different problem instances. The results show that, in the presence of timing constraints, GMP outperforms CPLEX in the majority of test instances.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3070913