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Unmanned aerial vehicle obstacle avoidance and path planning method

A path planning and unmanned aerial vehicle technology, applied in vehicle position/route/height control, instruments, three-dimensional position/course control, etc., can solve problems such as difficult training and difficult path planning, and achieve robust intelligent body behavior , Solve the effect of low utilization rate of data samples and strong decision-making ability

Active Publication Date: 2021-07-13
NANJING UNIV
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AI Technical Summary

Problems solved by technology

Reinforcement learning for UAV path planning has been gradually applied. However, in the process of UAV path planning, reinforcement learning algorithms can only perform fine path planning in a small area, and focus more on providing UAV motion control. Strategy, training is difficult, and it is often not easy to focus on the overall path planning when the task space is large

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  • Unmanned aerial vehicle obstacle avoidance and path planning method
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  • Unmanned aerial vehicle obstacle avoidance and path planning method

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Embodiment Construction

[0036] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0037] step one:

[0038] Build a simulation simulator based on the Unity3D engine. The simulator provides a simulated environment for the interaction between the UAV and the environment, as well as the training of obstacle avoidance and path selection strategies. The simulated environment includes drones and various obstacles that cannot be directly overcome, and can simulate various mission environments. The simulator can provide the performance parameters of the UAV itself, including the dynam...

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Abstract

The invention discloses an unmanned aerial vehicle obstacle avoidance and path planning method, which combines Monte Carlo tree search and contrast reinforcement learning algorithms, overcomes the problem of insufficient signals of a GPS (Global Positioning System) in a specific environment, and realizes the obstacle avoidance and path selection functions of an unmanned aerial vehicle in a complex environment. The method includes the following steps: (1) constructing an environment simulator; (2) the unmanned aerial vehicle obtains observation information in the simulator, and the observation information is processed by using a deep neural network; (3) performing coarse-grained path planning by utilizing Monte Carlo tree search, and generating stage target points in the advancing path of the unmanned aerial vehicle for training of a subsequent reinforcement learning algorithm; (4) learning a fine control strategy and fine-grained path planning of the unmanned aerial vehicle by using reinforcement learning; (5) accelerating unmanned aerial vehicle training based on comparative learning. According to the method provided by the invention, the unmanned aerial vehicle has an autonomous decision-making capability in a complex environment with high difficulty coefficient and large uncertain factors, and can cope with emergencies to a certain extent to complete specific tasks.

Description

technical field [0001] The invention relates to a solution for unmanned aerial vehicle obstacle avoidance and path planning combined with Monte Carlo tree search (MCTS) and comparative reinforcement learning technology, belonging to the technical field of unmanned aerial vehicle flight control. Background technique [0002] With the rapid development and progress of science and technology, the performance of UAV has been significantly improved, and it has been widely used in both civilian and military fields in recent years. In the application of UAV technology, autonomous flight and navigation are extremely challenging tasks. This task is generally divided into three stages: environment perception, path planning, and UAV flight control, of which path planning is the basis. In some special scenarios, the GPS signal often has large errors or even wrong positioning, so the autonomous path planning and obstacle avoidance of UAVs are even more important. The path planning of th...

Claims

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Application Information

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IPC IPC(8): G05D1/10
CPCG05D1/101
Inventor 俞扬詹德川周志华沈维捷秦熔均袁雷庞竟成管聪黄宇洋
Owner NANJING UNIV
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