Global Path Planning for Mobile Robot Based on Improved Dijkstra Algorithm and Particle Swarm Optimization

A novel method of improved Dijkstra algorithm and particle swarm optimization is proposed to evaluate global path planning for mobile robot. The first step is to make the MAKLINK graph which is used to describe the working space of mobile robot. The limited length value of free linkage line is conducted to substitute the constant weights in the adjacent matrix, which is well correlated with the fact that the number of paths is drastically less than that using the conventional Dijkstra method. Then the particle swarm optimization is adopted to investigate the global path from the several possible paths. Therefore, the proposed method facilitates reducing the computing time which enhances the efficiency of particle swarm optimization when performs the global path planning for mobile robot. Furthermore, simulation result is provided to verify the effectiveness and practicability.

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Hybridizing Particle Swarm Optimization and Differential Evolution for the Mobile Robot Global Path Planning

International Journal of Advanced Robotic Systems ◽

10.5772/63812 ◽

2016 ◽

Vol 13 (3) ◽

pp. 86 ◽

Cited By ~ 13

Author(s):

Biwei Tang ◽

Zhanxia Zhu ◽

Jianjun Luo

Keyword(s):

Particle Swarm Optimization ◽

Path Planning ◽

Mobile Robot ◽

Differential Evolution ◽

Particle Swarm ◽

Swarm Optimization ◽

Global Path Planning

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A convergence-guaranteed particle swarm optimization method for mobile robot global path planning

Assembly Automation ◽

10.1108/aa-03-2016-024 ◽

2017 ◽

Vol 37 (1) ◽

pp. 114-129 ◽

Cited By ~ 14

Author(s):

Biwei Tang ◽

Zhu Zhanxia ◽

Jianjun Luo

Keyword(s):

Particle Swarm Optimization ◽

Path Planning ◽

Mobile Robot ◽

Particle Swarm ◽

Computation Time ◽

Random Disturbance ◽

Swarm Optimization ◽

Content Type ◽

Global Path Planning ◽

Self Adaptive

Purpose Aiming at obtaining a high-quality global path for a mobile robot which works in complex environments, a modified particle swarm optimization (PSO) algorithm, named random-disturbance self-adaptive particle swarm optimization (RDSAPSO), is proposed in this paper. Design/methodology/approach A perturbed global updating mechanism is introduced to the global best position to avoid stagnation in RDSAPSO. Moreover, a new self-adaptive strategy is proposed to fine-tune the three control parameters in RDSAPSO to dynamically adjust the exploration and exploitation capabilities of RDSAPSO. Because the convergence of PSO is paramount and influences the quality of the generated path, this paper also analytically investigates the convergence of RDSAPSO and provides a convergence-guaranteed parameter selection principle for RDSAPSO. Finally, a RDSAPSO-based global path planning (GPP) method is developed, in which the feasibility-based rule is applied to handle the constraint of the problem. Findings In an attempt to validate the proposed method, it is compared against six state-of-the-art evolutionary methods under three different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the path optimality. Moreover, the computation time of the proposed method is comparable with those of the other compared methods. Originality/value Therefore, the proposed method can be considered as a vital alternative in the field of GPP.

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