Convergence of S3C empowered distributed cooperative optimization for multi-unmanned surface vehicles

Abstract

With the progress and development of modern marine science and technology, Unmanned Surface Vehicles (USVs) have been applied in various maritime scenes, for instance, target information search of marine environment, port patrol, etc. However, communication is different at maritime from on land, facing bad weather and uncontrollable factors, which leads to errors in the fusion of target position and velocity information of USVs, and it is difficult to achieve optimal path planning of USVs cluster. In this paper, S3C (intelligent sensor, communication, computing and control) fusion network method for USVs is studied for the model of USVs cluster with uncertain model parameters and unknown marine environmental disturbances, so as to optimize the tracking trajectory of USVs cluster center with a reasonable and efficient scheme. Firstly, a cloud–edge–end communication network architecture is constructed to ensure that USVs cluster communicates within a safe distance. Secondly, the brain-inspired sensing module is established to combine the positions detected by the vision sensors, radar and other intelligent sensors carried by each USV with integrated algorithms to solve the problem of target detection and location in USVs cluster. Finally, a computing module based on distributed cooperative optimization algorithm, and a control module combining the Extended State Observer (ESO) and the Controller are constructed to settle USVs’ cluster’s control. The simulation results verify that S3C fusion framework for USVs proposed is reasonable and effective.