Robot Path Planning Method Combining Enhanced APF and Improved ACO Algorithm for Power Emergency Maintenance

Robot Path Planning Method Combining Enhanced APF and Improved ACO Algorithm for Power Emergency Maintenance

Wei Wang, Xiaohai Yin, Shiguang Wang, Jianmin Wang, Guowei Wen
Copyright: © 2023 |Pages: 17
DOI: 10.4018/IJITSA.326552
Article PDF Download
Open access articles are freely available for download

Abstract

Considering the limited adaptability of the existing substation inspection robot path planning (PP) algorithms, the authors propose a novel PP method for mobile robots (MR) based on the structure of the ultra-high voltage (UHV) substation inspection robot system. The proposed method combines the improved ant colony optimization (IACO) algorithm and the enhanced artificial potential field (EAPF) algorithm. To minimize the interference of the pheromones, they introduced a pheromone adjustment coefficient in the later iterations of the algorithm. Furthermore, they improved the global pheromone update method, which is beneficial to the MR to search for the optimal path (OP) rapidly. They constructed two environmental models using the grid method, and they used MATLAB to implement comparative experiments between the proposed algorithm and other advanced methods. The results demonstrate that the proposed algorithm outperforms other methods in terms of running time, convergence speed, and global optimization ability.
Article Preview
Top

Nowadays, many intelligent algorithms have been applied to the MRPP, such as the D* algorithm, ACO algorithm, APF method, and artificial fish swarm algorithm (Ab Wahab et al., 2020). Each of these algorithms has its own advantages and drawbacks. The advantages of different algorithms are combined to provide new methods for solving MRPP, such as the combination of the APF method and ACO algorithm (Wang et al., 2018) or the fusion of the particle swarm algorithm and the A* algorithm (Lian et al., 2020).

Complete Article List

Search this Journal:
Reset
Volume 17: 1 Issue (2024)
Volume 16: 3 Issues (2023)
Volume 15: 3 Issues (2022)
Volume 14: 2 Issues (2021)
Volume 13: 2 Issues (2020)
Volume 12: 2 Issues (2019)
Volume 11: 2 Issues (2018)
Volume 10: 2 Issues (2017)
Volume 9: 2 Issues (2016)
Volume 8: 2 Issues (2015)
Volume 7: 2 Issues (2014)
Volume 6: 2 Issues (2013)
Volume 5: 2 Issues (2012)
Volume 4: 2 Issues (2011)
Volume 3: 2 Issues (2010)
Volume 2: 2 Issues (2009)
Volume 1: 2 Issues (2008)
View Complete Journal Contents Listing