A chaotic genetic algorithm with polynomial mutation for warehouse robot path planning

International Journal of High Performance Systems Architecture, vol. 11, pp. 191–197

Abstract

To plan an efficient picking path for warehouse robots, a chaotic genetic algorithm with polynomial mutation is recommended in this paper. First, in order to improve the efficiency of the genetic algorithm, the chaotic theory is employed to design a population initialisation strategy, which can increase the diversity of the initial population. Second, on the basis of the mutation operator designed based on polynomial mutation, the algorithm's capacity for diversity preservation can be enhanced. Third, two novel adaptive adjustments are presented for crossover and mutation operations in order to achieve a balance between convergence and diversity. As assessment indices of the fitness function, the path length, turn timings, and running energy consumption of the robot are taken into considerations. Simulation results indicate that the suggested approach outperforms the basic genetic algorithm and the ant colony optimisation algorithm in terms of path length and energy consumption.

Authors 5

1. Gang Lin
2. Jiangtao Xu
   Affiliation as printed

   Hangzhou CITIC Senior Living CORP, Zhejiang, Hangzhou, 310002, China

3. Weijun Gao Aachen Faculty of Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Faculty of Mechanical Engineering, RWTH Aachen, Aachen, 52062, Germany

4. Xianjie Peng

   Tongji University

   Affiliation as printed

   Department of Human Resources, Tongji University, Shanghai, 200092, China

5. Chengming Yang

   Tsinghua University

   Affiliation as printed

   Institute of Education, Tsinghua University, Beijing, 100084, China