Quad-rotor unmanned aerial vehicle suspension air transportation system trajectory planning method based on reinforcement learning

A quad-rotor UAV and reinforcement learning technology, applied in control/regulation systems, non-electric variable control, instruments, etc., can solve the problems of untested effects, high model accuracy requirements, and poor robustness.

Active Publication Date: 2020-09-04
TIANJIN UNIV
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Problems solved by technology

[0007] Based on the above research status, it can be seen that the current trajectory planning methods for the quadrotor UAV suspension air transport system still have many deficiencies, for example: 1) Some offline trajectory planning methods need to collect a large amount of flight experiment data and repeatedly iterate the variable information of the system Training and calculation are more complicat...

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  • Quad-rotor unmanned aerial vehicle suspension air transportation system trajectory planning method based on reinforcement learning
  • Quad-rotor unmanned aerial vehicle suspension air transportation system trajectory planning method based on reinforcement learning
  • Quad-rotor unmanned aerial vehicle suspension air transportation system trajectory planning method based on reinforcement learning

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[0094] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments, which are not intended to limit the protection scope of the present invention.

[0095] The present invention firstly constructs the performance index evaluation function, brings the index function into the Hamilton-Jacobi-Bellman equation according to the principle of optimality, and then updates the output layer weights of the execution network and the evaluation network in real time. , obtain the approximate solution of the HJB equation, and obtain the optimal control quantity.

[0096] like figure 2 As shown, the reinforcement learning-based trajectory planning method of the quadrotor UAV suspended air transport system of the present invention specifically includes the following steps:

[0097] Step 1, establish the dynamic model of the suspended air transport system of the quadrotor UAV:

[0098] like figure 1 As shown, i...

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Abstract

The invention discloses a quad-rotor unmanned aerial vehicle suspension air transportation system trajectory planning method based on reinforcement learning. The method comprises the steps that firstly, a performance index evaluation function is constructed; according to the optimization principle, the index function is substituted into a Hamilton-Jacobi-Bellman equation, and then the approximatesolution of the HJB equation is solved by updating the output layer weights of an execution network and an evaluation network in real time, so that the optimal control quantity is obtained. Compared with the prior art, the method has the advantages that the influence caused by unknown disturbance of the flight environment can be effectively suppressed, and trajectory planning and accurate positioncontrol of the unmanned aerial vehicle suspension flight system are realized.

Description

technical field [0001] The invention relates to a four-rotor unmanned aerial vehicle suspended air transport system, in particular to a flight trajectory planning method of a four-rotor unmanned aerial vehicle suspended air transport system. Background technique [0002] At present, the flight trajectory planning method of the four-rotor UAV suspended air transport system is divided into two categories: offline trajectory planning and online trajectory planning according to the stage of trajectory design generation. [0003] For offline trajectory planning, the more commonly used methods in the field of UAV suspended air transport systems include trajectory planning methods based on differential smoothing and trajectory planning methods based on optimal control ideas. Among them, the trajectory planning method based on differential smoothing uses the differential form of the original nonlinear system to map the state space of the original system into a low-dimensional smooth...

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Application Information

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IPC IPC(8): G05D1/10
CPCG05D1/101
Inventor 鲜斌韩晓薇蔡佳明
Owner TIANJIN UNIV
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