Optimal path training method for unmanned aerial vehicle to avoid columnar obstacle to reach target point

Abstract

The invention discloses an optimal path training method for an unmanned aerial vehicle to avoid a columnar obstacle to reach a target point. The method comprises the following steps of (1) constructing an environment simulator based on aerodynamics, (2) initializing an unmanned aerial vehicle strategy model by using a deep neural network, (3) enabling the unmanned aerial vehicle to obtain observation at the current moment in the environment simulator and making actions, and (4) enabling the unmanned aerial vehicle to interact with the environment simulator, generating and storing training data, sampling the training data, learning the unmanned aerial vehicle to avoid columnar obstacle through a reinforcement learning algorithm by using the training data, and selecting an optimal path to reach a target point. According to the method, the unmanned aerial vehicle can obtain learning ability similar to human beings, and the task is completed in tasks which are high in difficulty coefficient, unknown and complex in environment and have uncertain factors.

Description

technical field [0001] The invention relates to a UAV flight path planning and UAV control method, in particular to an optimal path training method for a UAV to avoid columnar obstacles and reach a target point, and belongs to the technical field of UAV flight control. Background technique [0002] Path planning is one of the most important links in robot navigation control. It means that the robot searches for an optimal, approximately optimal or suboptimal route from the starting point to the end point according to performance indicators such as time and distance. This is of great significance in real life. For example, in the logistics sorting system of modern e-commerce, the ground robots that shuttle back and forth need to carry out strict path planning to achieve obstacle avoidance between robots and deliver goods to designated locations. Finish sorting. The drone cargo delivery achieved in recent years is inseparable from strict path planning. At the same time, in t...

AI Technical Summary

Problems solved by technology

[0006] Generally, the UAV path planning algorithm using reinforcement learning has the defects of slow training speed and poor robustness

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