A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

Abstract

As one of the challenging tasks of multiple autonomous underwater vehicles systems, the realization of target hunting is the great significance. The multiple autonomous underwater vehicle target hunting is studied in this article. In some research, because the hunting members cannot reach the hunting point at the same time, the hunting time is long or the target escapes. To improve the efficiency of the target hunting, the leader–follower formation algorithm is introduced. Firstly, the task is assigned based on the distance between the autonomous underwater vehicle and the target. Then, the autonomous underwater vehicles with the same task are formed based on leader–follower mode, and the formation is kept to track the target. In the final capture phase, multiple autonomous underwater vehicle system use angle matching algorithm to round up target. The simulation results show that the proposed algorithm can effectively accomplish the target hunting task, save the hunting time, and avoid the target escape. Compared with the bioinspired neural network algorithm, the proposed algorithm shows better performance.

A Behavior-Driven Coordination Control Framework for Target Hunting by UUV Intelligent Swarm

“…For target hunting in UIS, if local control approach is directly employed, more hunter agents will be crowded by executing the same behavior, which can cause behavioral aggregation in tracking and capturing process. Furthermore, some early experiments have reported that swarm size should not be too large, it is easy to induce resource waste and computing explosions [4], [6], [20], [22], [25], [41]. Hence, a behavioralintensity control strategy which is inspired by clonal selection theory, is developed to flexibly determine which intensity value is maintained.…”

“…Although this method is effective, behavior exploration does not avoid in a particular situation where illegal behavior is evaluated due to the fact that one hunter exists more than one direction to move. Cao and Guo [6] developed a leaderfollower formation algorithm to assign hunting tasks by multiple autonomous underwater vehicles, and an angle matching strategy was utilized to guide round up target by leading hunter, however its topology is rigid to describe the position of agents, which can cause imperfect robustness, especially for large-scale agents. In the aforementioned works, centralized approaches are capable of achieving target hunting, however drawbacks cannot be ignored in dramatically changing environment.…”

Hunting Algorithm for Multi-AUV Based on Dynamic Prediction of Target Trajectory in 3D Underwater Environment

“…Five AUVs are distributed in the 3D underwater environment, their original positions are (7,45,18), (9,28,5), (19,9,3), (4,45,14), and (27,49,46). They are represented by dots in different colors.…”

“…The AUVs complete the hunting at the target marching point (32,38,37). Under the same conditions, the proposed algorithm uses five AUVs to successfully close the target at (28,25,22), and the hunting distance is shortened by 48%, as shown in Figure 9(b). By analyzing the reasons, it can be seen that the path selection of AUV hunting in a bioinspired neural network algorithm adopts the trajectory tracking strategy, and AUV always follows the target.…”

“…Among them, two AUVs are directly above and directly below the target, and the other three AUVs are distributed around the target in the same plane. So AUV distribution of the same plane is mainly considered in target hunting [28]. Therefore, the conditions for successful hunting of three AUVs in the same plane are defined as: when all the hunting AUVs are evenly distributed around the target and less than the specified distance, the target hunting is considered successful, as shown in equation (1).…”