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Swarm intelligence based dynamic obstacle avoidance for mobile robots under unknown environment using WSN

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Abstract

To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.

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Authors and Affiliations

  1. College of Electromechanical Engineering and Automation, National University of Defense Technology, Changsha, 410073, China

    Han Xue  (薛 晗) & Hong-xu Ma  (马宏绪)

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  1. Han Xue  (薛 晗)
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  2. Hong-xu Ma  (马宏绪)
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Corresponding author

Correspondence to Han Xue  (薛 晗).

Additional information

Foundation item: Project(60475035) supported by the National Natural Science Foundation of China

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Xue, H., Ma, Hx. Swarm intelligence based dynamic obstacle avoidance for mobile robots under unknown environment using WSN. J. Cent. South Univ. Technol. 15, 860–868 (2008). https://doi.org/10.1007/s11771-008-0158-9

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  • DOI: https://doi.org/10.1007/s11771-008-0158-9

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