Unmanned Surface Vehicle Course Planning for Deflection and Energy Control
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Solution Overview
Problem
Current course planning and control technologies for unmanned surface vehicles lack intelligent planning capabilities and global optimization, making them ineffective in complex water environments.
Innovation Solution
An intelligent course planning method is developed, which constructs a dynamic system, transforms its form, designs a cost function and performance index, uses a neural network model, and outputs a target course expression to achieve optimal navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional course control methods are used, then single issue control requirements are met, but intelligent planning capability and global optimization are lacking
Solution Approach 1:
The course control problem is segmented into multiple sub-objectives represented by different items in the cost function (course deviation, control effort, time constraints). Each sub-objective is optimized independently through the A* algorithm, allowing the system to handle complex planning requirements while maintaining manageable computational complexity.
Solution Approach 2:
A cost function serves as an intermediary between the control objectives and the A* optimization algorithm. This cost function translates multiple control requirements into a unified mathematical framework that the algorithm can process, enabling intelligent planning without directly increasing system complexity.
2Manufacturing precision
If global optimization is implemented, then overall course optimization is achieved, but computational complexity increases
Solution Approach 1:
The A* algorithm performs preliminary global optimization by pre-calculating the optimal course path based on the cost function before execution. This preliminary action ensures high precision in course planning while reducing real-time computational burden during actual navigation.
Solution Approach 2:
The cost function is designed to be dynamic, allowing adjustment of weighting factors for different control objectives based on operational conditions. This dynamic特性 enables the system to maintain high precision across varying scenarios without requiring complex recomputation of the entire optimization framework.
Data Source
AI summary
An intelligent course planning method and controller for unmanned surface vehicle are provided, an overall optimization from starting point to end point of course deflection angle is achieved based on an optimization principle of approximate dynamic programming; an optimization of minimization of the course deflection angle and a control signal is achieved based on a quadratic form cost function and a performance index by designing a virtual radial basis function neural network and a least square method, a target course expression is obtained, and a stability and a degree of convergence are ensured through a positive-definite constraint of the Hessian matrix of the performance index. Compared with the related art, an overshoot of course deflection and the control signal is reduced, an optimization of flight and steering energy consumption is achieved, and completely data-driven for intelligent course planning for unmanned surface vehicle and a high-accuracy feedback adjustment are achieved.