Behavior-Based Formation Control Digital Twin for Multi-AUG in Edge Computing

Abstract

The new generation of artificial intelligence technology has improved the autonomous monitoring capabilities of marine equipment. The ocean monitoring platform based on edge computing realizes the autonomous collaboration of multi-agent equipment groups. Autonomous Underwater Glider (AUG) is a new type of energy-saving marine equipment that can realize long-range ocean exploration. However, the non-negligible power constraints, time delays, communication failures and other unfavorable factors in the special underwater working environment have brought great challenges to the underwater monitoring operations of multi-AUG systems. This research establishes an improved artificial potential field method scheme based on the Maritime Internet of Things, which is based on the AUG leader's edge device to control multi-AUGs. In this process, an improved artificial potential field method is designed to solve the local optimal problem through behavior-based path optimization. Then, multi-AUGs are controlled to adapt to the task team plan based on the edge computing of the AUG leader. From the experimental results, it effectively realizes the AUG group cooperative control in the leader mode. Meanwhile, we established a marine communication model and AUG physics engine control model to complete a digital twin of multi-AUG monitoring tasks.