Control method and system for collaborative interception by multiple unmanned surface vessels
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Solution Overview
Problem
Current unmanned surface vessel systems lack an effective method for collaborative interception of intruding targets, resulting in low interception success rates and unclear task allocation during maritime escort tasks.
Innovation Solution
A control method and system utilizing a multi-agent deep deterministic policy gradient network structure and Kalman filter to accurately intercept intruding targets by obtaining task environment information, estimating interception points, and determining execution actions for each unmanned surface vessel within a group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple unmanned surface vessels are used for maritime escort, then the scope of maritime operations is expanded and complex tasks can be completed, but the task allocation and collaborative control become unclear and difficult
Solution Approach 1:
The patent divides the collaborative interception task into independent sub-tasks for each unmanned surface vessel. Each vessel is assigned a specific interception policy model that operates independently based on its own state and target information, eliminating the need for complex centralized coordination while maintaining effective multi-vessel operation.
2Reliability
If traditional interception methods are used by single unmanned surface vessel, then the system is simple to control, but the interception success rate is low
Solution Approach 1:
The patent replaces traditional mechanical control methods with intelligent algorithms. Each unmanned surface vessel is equipped with an interception policy output model that uses deep reinforcement learning to automatically determine optimal interception actions, substituting complex manual or centralized mechanical control with autonomous intelligent decision-making.
Solution Approach 2:
Each unmanned surface vessel independently determines its own interception policy through its dedicated interception policy output model. The system enables self-service by allowing each vessel to autonomously make interception decisions based on its own state information and the target's state, without requiring complex inter-vessel coordination or centralized control.
3Productivity
If centralized control is implemented for multiple unmanned surface vessels, then task allocation can be optimized, but the real-time response capability and system reliability decrease
Solution Approach 1:
The patent segments the centralized control function into distributed independent decision-making units. Each unmanned surface vessel has its own interception policy output model that independently processes local information and generates control commands, eliminating communication delays and single-point failures associated with centralized control while maintaining efficient task allocation.
Solution Approach 2:
The patent implements dynamic autonomous decision-making where each unmanned surface vessel can independently adapt its interception strategy in real-time based on changing conditions. This dynamic local decision-making capability allows the system to respond immediately to target movements and environmental changes without waiting for centralized control commands, significantly improving real-time response capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate collaborative interception of intruding targets by multiple unmanned surface vessels, enhancing the performance and efficiency of maritime escort operations.
Implementation Method 1
estimating interception point information of the intruding target at the current moment by using a Kalman filter according to the task environment information of all unmanned surface vessels at the current moment; the interception point information includes a predicted position of the intruding target and a predicted velocity of the intruding target
Data Source
AI summary
A control method and system for collaborative interception by multiple unmanned surface vessels are provided. The method includes obtaining task environment information of each unmanned surface vessel in an unmanned surface vessel group at a current moment, estimating interception point information of the intruding target at the current moment by using a Kalman filter according to the task environment information of the unmanned surface vessels at the current moment, determining process state information of each unmanned surface vessel at the current moment, inputting the process state information of each unmanned surface vessel at the current moment into a corresponding intruding target interception policy output model respectively to obtain an execution action of each unmanned surface vessel at a next moment to intercept the intruding target. The application can intercept the intruding target accurately.