ABSTRACT
It is reported that intelligent devices frequently invade the territorial waters of other countries to obtain relevant marine resource information. Due to the intelligence and flexibility of the Autonomous Underwater Vehicle (AUV), it is the best choice for encircling intrusion equipment. Encircling intelligent intrusion devices is a tough task, and a single AUV is less capable of meeting the requirements. To improve the efficiency of encircling tasks, task assignment is important and indispensable. This chapter summarizes the task assignment algorithms and applies a time-based Self Organized Mapping algorithm for task assignments. The path planning for pursuing the intrusion devices is also a key factor to ensure fast encirclement. After the review of path planning algorithms, to achieve rapid and dynamic tracking, an algorithm based on an improved GBNN (Glasius Bio-inspired Neural Network) is proposed. Simulation studies are conducted in 2-D and 3-D underwater environments. The results show that it can efficiently fulfill the encircling task through the proposed algorithm. Besides, through the comparison simulation, its efficiency is better than the distance-based task assignment and the BNN (bio-inspired neural network) algorithm.
