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conference paper · International Symposium on Computational Intelligence and Design · 2014

Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning

Li Zhou, Wei Li

Cited by 72Abstract and details only

Abstract

This paper presents an adaptive artificial potential field method for robot's obstacle avoidance path planning. Despite the obstacle avoidance path planning based on the artificial potential field method is very popular, but there is local minima problem with this approach. As a result, this paper proposes an improved obstacle potential field function model considering for the size of the robot and the obstacles and changes the weight of the obstacle potential field function adaptively to make the robot escape from the local minima. Three simulations have been done and the simulation results show: the improved algorithm can make the robot escape from the local minima and accomplish the robot collision avoidance path planning well.

Cite this paper

APA 7

Zhou, L., & Li, W. (2014). Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning. International Symposium on Computational Intelligence and Design, 429–432. https://doi.org/10.1109/iscid.2014.144

MLA 9

Zhou, Li, and Wei Li. “Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning.” International Symposium on Computational Intelligence and Design, 2014, pp. 429–32, https://doi.org/10.1109/iscid.2014.144.

Chicago

Zhou, Li, and Wei Li. 2014. “Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning.” International Symposium on Computational Intelligence and Design: 429–32. https://doi.org/10.1109/iscid.2014.144.

Harvard

Zhou, L. and Li, W. (2014) ‘Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning’, International Symposium on Computational Intelligence and Design, pp. 429–432. Available at: https://doi.org/10.1109/iscid.2014.144.

BibTeX
@article{zhou2014adaptive,
  title = {{Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning}},
  author = {Zhou, Li and Li, Wei},
  journal = {International Symposium on Computational Intelligence and Design},
  pages = {429--432},
  year = {2014},
  doi = {10.1109/iscid.2014.144}
}

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