TY - GEN
T1 - A method for solving TSP of steel pylon inspection by magnetic adsorption robots based on improved ant colony optimization
AU - Jiang, Guojie
AU - Wang, Peng
AU - Wei, Shaobin
AU - Tang, Zhihui
AU - Wen, Yufang
AU - Gao, He
AU - Tao, Yong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - For the problem of multi-point inspection of steel pylons by robots for very large bridges, the input inspection path points are abstracted as nodes and the weight of each path point is calculated according to the actual situation of the project, so as to establish a weighted undirected graph of the movement of magnetic adsorption robots between each point. The original problem can then be transformed into the problem of finding the shortest possible path (the minimum cost) for each node in a given group, visiting each node once and returning to the origin, namely the traveling salesman problem (TSP). In this study, a path optimization method for magnetic adsorption robots for steel pylons based on an improved ant colony optimization (ACO) algorithm is proposed, and the Euclidean TSP is solved using the improved ACO approach (IACO). Finally, the simulation results demonstrate that IACO has advantages in convergence speed and stability as compared to conventional ACO, which proves the validity of the proposed method.
AB - For the problem of multi-point inspection of steel pylons by robots for very large bridges, the input inspection path points are abstracted as nodes and the weight of each path point is calculated according to the actual situation of the project, so as to establish a weighted undirected graph of the movement of magnetic adsorption robots between each point. The original problem can then be transformed into the problem of finding the shortest possible path (the minimum cost) for each node in a given group, visiting each node once and returning to the origin, namely the traveling salesman problem (TSP). In this study, a path optimization method for magnetic adsorption robots for steel pylons based on an improved ant colony optimization (ACO) algorithm is proposed, and the Euclidean TSP is solved using the improved ACO approach (IACO). Finally, the simulation results demonstrate that IACO has advantages in convergence speed and stability as compared to conventional ACO, which proves the validity of the proposed method.
UR - https://www.scopus.com/pages/publications/85123281197
U2 - 10.1109/WRCSARA53879.2021.9612683
DO - 10.1109/WRCSARA53879.2021.9612683
M3 - 会议稿件
AN - SCOPUS:85123281197
T3 - 2021 WRC Symposium on Advanced Robotics and Automation, WRC SARA 2021
SP - 188
EP - 194
BT - 2021 WRC Symposium on Advanced Robotics and Automation, WRC SARA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd WRC Symposium on Advanced Robotics and Automation, WRC SARA 2021
Y2 - 11 September 2021 through 11 September 2021
ER -