The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.
This article presents a consistency control algorithm for the autonomous underwater vehicle (AUV) group combined with the leader–follower approach under communication delay. First, the six-degree-of-freedom (DoF) model of AUV is represented, and the graph theory is used to describe the communication topology of the AUV group. Especially, a hybrid communication topology is introduced to adapt to large formation control. Second, the distributed control law is constructed by combining the consensus theory with the leader–follower method. The consistency control algorithms for homogeneous and heterogeneous AUV groups based on the leader–follower approach under communication delay are proposed. Stability criteria are established to guarantee the consensus based on the Gershgorin disk theorem and Nyquist law, respectively. Finally, numerous simulation experiments are carried out to show the effectiveness and superiority of the proposed algorithms.
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