Technological innovation and application validation of agile collaboration for heterogeneous marine unmanned clusters: architecture, methodology and controller

To address issues such as unclear application architecture, weak information interaction capability, and low efficiency in the collaborative testing of heterogeneous marine unmanned clusters, this paper proposes an agile collaboration technology. This approach enables system-level clusters to quickly respond to task demands platform-level unmanned system to integrate swiftly into the cluster, and system-level controllers decouple software and hardware step-by-step for agile task processing and execution.

First, this study analyzes the task characteristics, network and optimization requirements for the cooperative application scenarios of marine unmanned clusters, and divides them into several node groups according to their functions. Then, it can design the heterogeneous marine unmanned cluster agile cooperative architecture based on the functional node groups, and carry out the load complementation and task coordination through the fusion configuration of the cooperative architecture. Next, based on the application requirements and architectural features, the common marine unmanned cluster application tasks are divided into three categories: time priority tasks, sequential execution tasks, and routine operation tasks, and an agile task planning method based on the self-organizing graph algorithm is proposed for the heterogeneous marine unmanned clusters. Finally, a multilevel software-hardware decoupled marine unmanned cluster agile collaborative controller is developed, which interacts with the various systems of the platform in the form of a unified interface.

According to the test results of dynamic target detection and tracking on-lake experiments by heterogeneous unmanned clusters, the final average heading deviation angle between each vehicle and the target is 6.7°, which successfully accomplished the cooperative detection and tracking of dynamic targets.

This method can enable marine unmanned systems with significant performance disparities to quickly integrate into and implement typical tasks, and has excellent scalability and adaptability, which can help accelerate the development and practice of marine unmanned clusters.