Path planning for mobile robot using self-adaptive learning particle swarm optimization

Research Paper
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Abstract

As a challenging optimization problem, path planning for mobile robot refers to searching an optimal or near-optimal path under different types of constrains in complex environments. In this paper, a self-adaptive learning particle swarm optimization (SLPSO) with different learning strategies is proposed to address this problem. First, we transform the path planning problem into a minimisation multi-objective optimization problem and formulate the objective function by considering three objectives: path length, collision risk degree and smoothness. Then, a novel self-adaptive learning mechanism is developed to adaptively select the most suitable search strategies at different stages of the optimization process, which can improve the search ability of particle swarm optimization (PSO). Moreover, in order to enhance the feasibility of the generated paths, we further apply the new bound violation handling schemes to restrict the velocity and position of each particle. Finally, experiments respectively with a simulated robot and a real robot are conducted and the results demonstrate the feasibility and effectiveness of SLPSO in solving mobile robot path planning problem.

Keywords

path planning self-adaptive learning particle swarm optimization learning strategy learning mechanism boundary violations handling 

Notes

Acknowledgements

This work was supported by National Basic Research Program of China (973 Program) (Grant No. 2013CB035503).

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina
  2. 2.State Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijingChina

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