Path Planning Based on Clustering and Improved ACO in UAV-assisted Wireless Sensor Network

UAVs as mobile nodes have been introduced into wireless sensor network (WSN) to assist information transmission and reduce the burden of communication. To minimize the path cost of UAVs for information transmission, this paper focuses on the path planning of UAVs. A multi-UAVs path planning combined K-means clustering algorithm and improved MAX-MIN ant system (MMAS) is proposed. This algorithm apply the K-means clustering algorithm to reduce the problem size and improve the search efficiency of subsequent path planning. By modifying the node search rules and proposing two optimal solution detection rules of the MMAS, the algorithm searching stagnation and failing into local optimal solution are effectively avoided.