一种水下机器人协同操作任务分配方法
By constructing a motion allocation knowledge base and game model for a dual-robot-manipulator system, and utilizing an improved differential evolution algorithm, the problems of redundant degrees of freedom and limited energy for autonomous underwater robots operating collaboratively in complex marine environments were solved, achieving efficient and robust task allocation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HARBIN ENG UNIV
- Filing Date
- 2025-07-29
- Publication Date
- 2026-07-17
AI Technical Summary
In complex marine environments, multiple autonomous underwater robots equipped with robotic arms face issues of redundant degrees of freedom and limited energy when working together, resulting in low motion allocation efficiency, poor dynamic adaptability and robustness, making it difficult to complete tasks efficiently.
A knowledge base for motion allocation in a dual-robot-manipulator system is constructed. Through a multi-level knowledge representation model and a game theory model, a payoff function is designed, and an improved differential evolution algorithm is used to solve for the Nash equilibrium to optimize task allocation.
It achieves efficient and robust task allocation for multi-robot collaborative operations in complex marine environments, improving task efficiency and the accuracy of action allocation while satisfying the Nash equilibrium condition.
Smart Images
Figure CN120848563B_ABST