TY - GEN
T1 - Multi-UAV Cooperative 3D Coverage Path Planning Based on Asynchronous Ant Colony Optimization
AU - Li, Hui
AU - Chen, Yang
AU - Chen, Zhihuan
AU - Wu, Huaiyu
N1 - Publisher Copyright:
© 2021 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2021/7/26
Y1 - 2021/7/26
N2 - UAVs carrying visual sensors to capture the defects on the building surface has the advantages of high efficiency, low cost, flexibility and convenience. This kind of inspection task is called coverage path planning problem in 3D space. When the target to be detected is a large complex building, multi-UAV collaboration is usually required. So, how to obtain the optimal path of multi-UAV becomes a great challenge. In order to solve the problem, Asynchronous Ant Colony Optimization (AACO), which makes multiple ant colonies move forward asynchronously, is proposed here to conquer the difficulty. Firstly, the random sampling method is used to get the potential UAV waypoints in the 3D free environment, based on which a Primitive Coverage Graph (PCG) is constructed. Also, the visibility matrix and coverage rate are defined to quantify the coverage performance of UAV path primitives. Next, Asynchronous Ant Colony Optimization combined with reward strategy is proposed to solve the problem by selecting jump cities in turn. Finally, several simulations are provided to verify the feasibility and effectiveness of the algorithm.
AB - UAVs carrying visual sensors to capture the defects on the building surface has the advantages of high efficiency, low cost, flexibility and convenience. This kind of inspection task is called coverage path planning problem in 3D space. When the target to be detected is a large complex building, multi-UAV collaboration is usually required. So, how to obtain the optimal path of multi-UAV becomes a great challenge. In order to solve the problem, Asynchronous Ant Colony Optimization (AACO), which makes multiple ant colonies move forward asynchronously, is proposed here to conquer the difficulty. Firstly, the random sampling method is used to get the potential UAV waypoints in the 3D free environment, based on which a Primitive Coverage Graph (PCG) is constructed. Also, the visibility matrix and coverage rate are defined to quantify the coverage performance of UAV path primitives. Next, Asynchronous Ant Colony Optimization combined with reward strategy is proposed to solve the problem by selecting jump cities in turn. Finally, several simulations are provided to verify the feasibility and effectiveness of the algorithm.
KW - 3D Coverage Path Planning
KW - Asynchronous Ant Colony Optimization
KW - Path Primitive
UR - https://www.scopus.com/pages/publications/85117357353
U2 - 10.23919/CCC52363.2021.9549498
DO - 10.23919/CCC52363.2021.9549498
M3 - Conference contribution
AN - SCOPUS:85117357353
T3 - Chinese Control Conference, CCC
SP - 4255
EP - 4260
BT - Proceedings of the 40th Chinese Control Conference, CCC 2021
A2 - Peng, Chen
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 40th Chinese Control Conference, CCC 2021
Y2 - 26 July 2021 through 28 July 2021
ER -