A Method of Unmanned Aerial Vehicle False Path Planning Based on Deep Reinforcement Learning

Abstract

The invention discloses a method for unmanned aerial vehicle pseudo-path planning based on deep reinforcement learning. First, the boundary coordinates of the no-fly area are divided on the flight map, and the starting and ending coordinates of the flying mission of the unmanned aerial vehicle are marked; Before the mission, the current environmental state of the UAV is perceived, and the deep reinforcement learning algorithm is used to select the deflection angle and flight action in the current environment according to the obtained Q function value; the UAV continuously receives information from the ground base station transmitting equipment during the flight process. The Q function is updated with the reward reward obtained from the flight position data and interaction with the environment; during the flight, the no-fly area is used as a virtual obstacle to determine whether the drone is flying according to the preset route; if it is close to the edge of the no-fly area, pass The reward function guides the unmanned aerial vehicle to plan a pseudo-navigation path and avoid the no-fly area; the invention realizes the pseudo-path planning of the unmanned aerial vehicle in an unknown environment, and improves the intelligence and safety of the flying of the unmanned aerial vehicle.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a method for false path planning of unmanned aerial vehicles based on deep reinforcement learning. Background technique [0002] With the considerable progress in the field of computing and artificial intelligence, UAVs are used in more and more fields, especially in the field of military aviation, and the types of tasks performed by UAVs are becoming more and more complex. The fields of military reconnaissance and air transport have played an important role. The requirements for intelligent UAV trajectory planning are also getting higher and higher. When UAVs perform special tasks, UAVs must avoid normal civil aviation flight areas and Radar monitoring area, so as not to interfere with the flight of civil aviation aircraft and radar monitoring. In order to better serve applications in various fields, the research on UAV false path planning has become a res...

AI Technical Summary

Problems solved by technology

[0004] In the process of actually solving the UAV track path planning, according to different tasks and the complexity of the terrain environment, choose an intelligent algorithm that conforms to the track planning. The existing algorithm is based on the unmanned However, in actual situations, some no-fly areas in the airspace are undetectable invisible obstacles, and it is easy to mistakenly fly into the restricted area during the flight of the drone, causing other airspace flight hazards

Images