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• W3129718483 abstract "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." @default.
• W3129718483 created "2021-03-01" @default.
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• W3129718483 date "2021-02-20" @default.
• W3129718483 modified "2023-09-27" @default.
• W3129718483 title "Generation of Optimal Coverage Paths for Mobile Robots Using Hybrid Genetic Algorithm" @default.
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• W3129718483 doi "https://doi.org/10.20965/jrm.2021.p0011" @default.
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