Depth Q learning-based UAV (unmanned aerial vehicle) environment perception and autonomous obstacle avoidance method

Abstract

The invention belongs to the field of the environment perception and autonomous obstacle avoidance of quadrotor unmanned aerial vehicles and relates to a depth Q learning-based UAV (unmanned aerial vehicle) environment perception and autonomous obstacle avoidance method. The invention aims to reduce resource loss and cost and satisfy the real-time performance, robustness and safety requirements ofthe autonomous obstacle avoidance of an unmanned aerial vehicle. According to the depth Q learning-based UAV (unmanned aerial vehicle) environment perception and autonomous obstacle avoidance methodprovided by the technical schemes of the invention, a radar is utilized to detect a path within a certain distance in front of an unmanned aerial vehicle, so that a distance between the radar and an obstacle and a distance between the radar and a target point are obtained and are adopted as the current states of the unmanned aerial vehicle; during a training process, a neural network is used to simulate a depth learning Q value corresponding to each state-action of the unmanned aerial vehicle; and when a training result gradually converges, a greedy algorithm is used to select an optimal action for the unmanned aerial vehicle under each specific state, and therefore, the autonomous obstacle avoidance of the unmanned aerial vehicle can be realized. The method of the invention is mainly applied to unmanned aerial vehicle environment perception and autonomous obstacle avoidance control conditions.

Description

technical field [0001] The invention relates to the field of environment perception and autonomous obstacle avoidance of a four-rotor UAV, especially in the field of research on intelligent path planning for designing UAVs. It specifically involves the UAV environment perception and autonomous obstacle avoidance method based on deep Q-learning. Background technique [0002] In recent years, UAV (Unmanned Aerial Vehicle, UAV) has gradually entered the public's field of vision and shines in the fields of commerce, agriculture, entertainment and even military affairs. In the past ten years, the number of drones in my country has grown from scratch to prosperity compared to before. The data shows that as of 2018, the consumption of civilian drones in my country alone has reached nearly 10 billion, and the consumption is showing a rapid upward trend. The prosperity of the UAV market has put forward higher requirements for the safety and development of UAV control technology. A...

AI Technical Summary

Problems solved by technology

The disadvantages of this scheme are: 1) There may be a delay in the transmission of data between the two modules, resulting in a lag in the safe path planned by the path planning algorithm, which affects the safe navigation of the UAV; 2) Data transmission is lost, Distortion phenomenon, causing the path planning part to lose reliable data support, and unable to respond to obstacles in a timely manner; 3) Most path planning algorithms tend to fall into local optimal solutions, and it is difficult to efficiently solve path planning problems in more complex flight environments

4) The distance sensing technology is easily affected by environmental factors such as weather. When the weather is bad or counter-interference occurs, accurate obstacle distance detection cannot be performed

In short, most of the current traditional UAV autonomous obstacle avoidance schemes use the way of perception and path planning to connect with each other, and it is necessary to ensure the maturity of their respective technologies and the efficient transmission of data between the two; , may cause the algorithm to fail, and the robustness is poor

Images