Search

A search method for uncertain targets using multiple autonomous underwater vehicles (AUVs) is studied. To improve search efficiency, a cooperative search method based on target clustering and path optimization is proposed to reduce reactive consumption and obtain more revenue. Firstly, all the target grids are connected and the target areas are clustered according to the theory of minimum spanning tree. Secondly, the moving paths of AUVs are studied and divided into two stages: transferring to a target area and detecting in a target grid. And both of the stages are then optimized. Finally, a series of experiments in different cases are carried out, and the cooperative search method through target clustering and path optimization is compared with another two algorithms. Results show that the cooperative search method proposed in this paper can obtain a larger amount of total revenue in the same period of time with more stable performance than the other algorithms. It is proved that this method conduces to a more thorough search of the target areas.

Rate of revenue (ROR) is significant for unmanned aerial vehicle (UAV) to search targets located in probabilistic positions. To improve search efficiency in a situation of multiple static targets, this paper first transfers a continuous area to a discrete space by grid division and proposes some related indexes in the UAV search issue. Then, cooperative strategies of multiple UAVs are studied in the searching process: clustering partition of search area based on minimum spanning tree (MST) theory is put forward as well as path optimization using spiral flying model. Finally, a series of simulation experiments are carried out through the method in this paper and two compared algorithms. Results show that: optimized cooperative strategies can achieve greater total revenue and more stable performance than the other two.

Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.