Work

Edge-Intelligence-Driven Cooperative Control Framework for Heterogeneous Unmanned Aerial and Surface Vehicles in Complex Maritime Environments

Year: 2025

Type: article

Abstract: With the increasing deployment of unmanned systems in maritime patrol, coastal monitoring, and environmental mapping, achieving effective UAV-USV collaboration in dynamic environments remains challenging. This paper proposes an edge-intelligence-driven collaborative control framework that integrates unified data modeling, multi-objective task scheduling, lightweight fault-tolerant middleware, and multi-sensor fusion. A Weighted Kalman Filter combines UAV imaging and USV sonar data to enhance perception accuracy, while NSGA-II optimizes task allocation considering completion time, energy consumption, and sensing reliability. The framework was validated through representative maritime scenarios, including patrol and coastal sediment mapping, on a virtual simulation platform. Results show improved task efficiency, energy utilization, communication latency, and robustness compared with single-platform and centralized scheduling approaches. The proposed method provides a balanced optimization of execution efficiency, energy consumption, data accuracy, and resilience, offering a reliable solution for large-scale maritime applications.

Source: Drones

DOI: 10.3390/drones9110755

Authors: Jingfeng Yang, Lingling Zhao, Bo Peng

Institutions: Guangdong Academy of Sciences, Guangzhou Institute of Geography, Jinan University

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137