Heterogeneous USV-AUV Formation Control With Quantum Weight Optimization
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Solution Overview
Problem
Current research focuses on formation control of either unmanned surface vehicles (USVs) or autonomous underwater vehicles (AUVs) separately, failing to effectively combine their functions for coordinated water surface-to-underwater operations, resulting in limited application and low task execution efficiency.
Innovation Solution
A heterogeneous agent formation control method based on a cloud-model-based quantum genetic algorithm is developed, establishing dynamics models for USVs and AUVs, classifying behaviors, and optimizing weight coefficients to implement integrated formation control, enhancing operating efficiency and coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single agent (USV or AUV) is used to execute tasks, then the system structure is simple, but the carrying capacity, coverage, and information processing capacity are limited
Solution Approach 1:
The patent combines multiple heterogeneous agents (USVs and AUVs) into a coordinated formation system. The control method integrates dynamics models of both surface and underwater vehicles, enabling them to work together as a unified system that pools their respective capabilities for enhanced carrying capacity, coverage, and information processing
2Productivity
If formation control is implemented with heterogeneous agents, then the operating efficiency and coverage are improved, but the control complexity increases
Solution Approach 1:
The control method segments the formation control problem into distinct behavioral components (move-to-goal, keep-formation, avoid-static-obstacle, avoid-dynamic-obstacle). Each behavior is independently modeled and then integrated through weighted combinations, making the complex control task more manageable and systematic
Solution Approach 2:
The patent uses a cloud-model-based quantum genetic algorithm to dynamically optimize the weight coefficients of different behaviors. This parameter optimization approach adapts the control strategy to varying conditions, improving operating efficiency while systematically managing control complexity through automated parameter tuning
3Ease of manufacture
If traditional genetic algorithms are used for optimization, then the implementation is straightforward, but the convergence speed is slow and local optima are easily trapped
Solution Approach 1:
The patent introduces a cloud model as an intermediary layer between the quantum genetic algorithm and the formation control system. The cloud model generates cloud droplets that guide the quantum genetic algorithm's search process, improving convergence speed and preventing local optima trapping while maintaining implementation feasibility
Data Source
AI summary
The disclosure provides a heterogeneous agent formation control method based on a cloud-model-based quantum genetic algorithm. Heterogeneous agents include a surface unmanned vehicle and an autonomous underwater vehicle. The control method includes the following steps: establishing a dynamics model of a surface unmanned vehicle and an autonomous underwater vehicle; designing formation behaviors of the heterogeneous agents based on a behavior algorithm and a leader-follower algorithm by using the established dynamics model; and optimizing weight coefficients of different behaviors of the heterogeneous agents based on a cloud-model-based quantum genetic algorithm by using the established dynamics model to obtain an optimal formation control strategy and implement formation control over the heterogeneous agents. The method disclosed by the present invention can implement integrated formation control over the surface unmanned vehicle and the autonomous underwater vehicle to improve operating efficiency and expand an operating range.