Autonomous navigation unmanned aerial vehicle power optimization method based on deep reinforcement learning

A reinforcement learning and autonomous navigation technology, applied in mechanical equipment, combustion engines, internal combustion piston engines, etc., can solve the large end-to-end delay and energy consumption of UAVs, performance is greatly affected by bandwidth fluctuations, and cannot provide power. performance and other issues, to achieve the effect of improving computing power utilization efficiency, improving battery life, and improving power consumption utilization.
CN112711271AActive Publication Date: 2021-04-27SUN YAT SEN UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
SUN YAT SEN UNIV
Publication Date
2021-04-27

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Abstract

The invention discloses a power optimization method based on deep reinforcement learning in an unmanned aerial vehicle driving application. The method achieves the low-delay and high-energy-efficiency autonomous navigation task execution through the dynamic configuration of the calculation scale of a convolutional neural network in combination with the state features of an environment where an unmanned aerial vehicle is located. The method comprises the following steps of firstly, designing and training a deep neural network capable of receiving input layers of different sizes, and calculating the control direction and speed of the unmanned aerial vehicle according to the image input of a front camera, and then, by utilizing reinforcement learning, according to the environment complexity, the obstacle hybrid factor and the historical action vector of the current time block, deducing the optimal neural network configuration of the calculation power consumption suitable for the current environment, so that the utilization rate of the calculation energy consumption of the unmanned aerial vehicle equipment is improved, and the endurance time of the autonomous navigation unmanned aerial vehicle is prolonged.
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Description

technical field

[0001] The present invention relates to the technical fields of edge computing, deep learning, reinforcement learning and automatic driving, and more specifically, relates to a power optimization method for autonomous navigation UAV based on deep reinforcement learning. Background technique

[0002] In recent years, the autonomous navigation capability of drones has attracted widespread attention from the robotics community. The advantages of autonomous navigation drones, such as easy deployment, agility and mobility, have been widely used in many fields, such as fire detection , precision agriculture, express delivery and security inspections, etc. The traditional way to realize self-navigation is to use SLAM algorithm, which includes two processes of perception of a given map and calculation of control commands. However, separating the perception process from the control process not only hinders the positive feedback between the perception process and the ...

Claims

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