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

• Main Authors: Hongtao Liang, Yanfang Fu, Fengju Kang, Jie Gao, Ning Qiang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Target hunting; behavior-driven; coordination control; hybrid non-central topology; dual-layer switching control
• Online Access: https://ieeexplore.ieee.org/document/8948297/

One of most primitive problems by unmanned underwater vehicle intelligent swarm (UIS) is coordination control, which has a great significance for realization of target hunting with great performance of efficiency and robustness. Existing studies concentrate on behavior-based centralized or distributed control approaches with the prior knowledge and mostly do not elaborately consider behavior conflicts and constraint differences. Therefore, a novel behavior-driven coordination control framework including topology architecture and swarm control which is inspired by immune mechanism, is investigated for target hunting of heterogeneous UIS under unknown and uncertain environment in this paper. For topology architecture, a hybrid non-central distributed topology is developed as a novel immune-inspired architecture to regulate agents with self-organizational and fault-tolerance features. For swarm control, a dual-layer switching control scheme composed by global control and local control, is proposed to drive behaviors via behavioral-intensity, the trigger of switching is when the target is detected. The global control approach is employed to search target, in which two constraints of energy consumption and healthy-state are considered to achieve good operational reliability. While the local control approach is developed to form the dynamic alliance of tracking and capturing, in which behavioral-intensity control strategy for behavior aggregation and decision-making control strategy for behavior selection are respectively designed to avoid behavior conflicts. Simulation results demonstrate that proposed framework can accomplish hunting under various situations such as hunter agent is random or fixed distribution, and the number of targets asynchronously appears. It is confirmed that our framework is capable of achieving the target hunting under unknown and uncertain environment with greater efficiency and robustness.