A Method of Intelligent Local Path Planning for Unmanned System Based on Double Backpropagation Neural Network

Abstract

The invention relates to an intelligent local path planning method for an unmanned system based on dual backpropagation neural networks. Aiming at the dynamic obstacle avoidance problem of the intelligent unmanned system, firstly, an environment model for the operation of the intelligent unmanned system is established; secondly, the unmanned system is established The kinematic model of the operation; thirdly, a double back-propagation neural network with a topological structure composed of two back-propagation neural networks is designed to perform local path planning. The first back-propagation neural network undergoes appropriate offline training and is dynamically Playing a leading role in obstacle avoidance, the second backpropagation neural network is trained online to obtain compensation for the output; finally, this method can be used for intelligent unmanned systems such as drones, unmanned vehicles, and unmanned ships to avoid static obstacles. and dynamic obstacles. For obstacles with changing speeds, this method can predict and avoid obstacles based on their previous movements, reducing the number of decisions and computational burden on the intelligent unmanned system, and improving collision avoidance performance and efficiency.

Description

technical field [0001] The invention relates to an intelligent local path planning method for an unmanned system based on a double backpropagation neural network, which can be applied to intelligent unmanned systems such as unmanned aerial vehicles, unmanned vehicles, and unmanned ships for local path planning to avoid static obstacles and dynamic obstacles. Background technique [0002] With the rapid development of artificial intelligence, the use of intelligent unmanned systems to complete specific tasks has attracted more and more attention from the industry. Path planning is divided into global path planning and local path planning. The global path planning, that is, the environment information is completely known to the unmanned system. Local path planning means that in a dynamic environment with unknown obstacles, the intelligent unmanned system uses its own sensor system to perform fusion processing based on the real-time perceived environmental information and cha...

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Problems solved by technology

[0009] The present invention aims at the shortcomings in the above local path planning method that the unmanned system cannot perform continuous and accurate obstacle avoidance, too many times of planning, and cannot obtain the global optimal solution, and provides a new double backpropagation neural network (backpropagation neural network) ) topology, which improves the obstacle avoidance efficiency of the mobile unmanned system and reduces the computational pressure

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