Generation of Optimal Coverage Paths for Mobile Robots Using Hybrid Genetic Algorithm

This paper presents new optimal offline approaches to solve the coverage path planning problem. A novel hybrid genetic algorithm (HGA), which uses, the turn-away starting point and backtracking spiral algorithms for performing local search, is proposed for grid-based environmental representations. T...

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Veröffentlicht in:Journal of robotics and mechatronics Jg. 33; H. 1; S. 11 - 23
Hauptverfasser: Schäfle, Tobias Rainer, Mitschke, Marcel, Uchiyama, Naoki
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Tokyo Fuji Technology Press Co. Ltd 20.02.2021
Schlagworte:
Energy value
Fitness
Genetic algorithms
Path planning
Travel time
ISSN:0915-3942, 1883-8049
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Zusammenfassung:This paper presents new optimal offline approaches to solve the coverage path planning problem. A novel hybrid genetic algorithm (HGA), which uses, the turn-away starting point and backtracking spiral algorithms for performing local search, is proposed for grid-based environmental representations. The HGA algorithm is validated using the following three different fitness functions: the number of cell visits, traveling time, and a new energy fitness function based on experimentally acquired energy values of fundamental motions. Computational results show that compared to conventional methods, HGA improves paths up to 38.4%; moreover, HGAs have a consistent fitness for different starting positions in an environment. Furthermore, experimental results prove the validity of the fitness function.
Bibliographie:ObjectType-Article-1
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ISSN:0915-3942
1883-8049
DOI:10.20965/jrm.2021.p0011