Generation of Optimal Coverage Paths for Mobile Robots Using Hybrid Genetic Algorithm
Tobias Rainer Schäfle*, Marcel Mitschke**, and Naoki Uchiyama*
*Department of Mechanical Engineering, Toyohashi University of Technology
1-1 Hibarigaoka, Tempaku, Toyohashi, Aichi 441-8580, Japan
**Andreas Stihl AG & Co. KG
Badstraße 115, Waiblingen 71336, Germany
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.
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