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Automatic driving control method of distribution car based on deep q network

A control method and automatic driving technology, applied in non-electric variable control, two-dimensional position/channel control, vehicle position/route/height control, etc., can solve the problem of high cost, speed up the training process, and avoid the loss of delivery vehicles Effect

Active Publication Date: 2021-06-18
SUZHOU UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

The current unmanned control vehicles mainly use radar sensors to measure the distance between the vehicle and obstacles. This control method is expensive, making it difficult to promote it on a large scale in unmanned delivery vehicles.

Method used

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  • Automatic driving control method of distribution car based on deep q network
  • Automatic driving control method of distribution car based on deep q network
  • Automatic driving control method of distribution car based on deep q network

Examples

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Embodiment

[0030] Example: see attached Figure 1~3 As shown, an automatic driving control method based on a deep Q network distribution car, including a sensor system, a control system, a drive system and a power system, the sensor system collects environmental information and power system information, and combines the environmental information and power system The system information is transmitted to the control system, and the control system processes the received information through a self-learning control method, and then the sensor system receives the control information to control the movement state of the distribution car.

[0031] In this embodiment, the overall control framework is a deep Q-Network (DeepQ-Network, DQN) in deep reinforcement learning, and a Q-learning (Q-Learning) algorithm in the field of reinforcement learning is used for control. Assume that at each time step t=1,2,..., the state of the Markov decision process observed by the unmanned control car sensor syste...

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Abstract

The invention discloses an automatic driving control method for a distribution car based on a deep Q network, which is characterized in that it includes a sensing system, a control system, a driving system and a power system, the sensing system collects environmental information and power system information, and The environment information and the power system information are transmitted to the control system, and the control system processes the received information through a self-learning control method to control the movement state of the distribution trolley. The present invention uses a deep reinforcement learning optimization method with a safe distance in the control system of the unmanned trolley, processes the environmental information obtained from the sensor system, then selects an appropriate action, and uses the sensor system to transmit the control signal of the control system To the drive system, the unmanned control car performs corresponding actions to adapt to the ever-changing road environment.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and control technology, and in particular relates to an automatic driving control method of a distribution car based on a deep Q network, which can perform self-learning and complete the control of an unmanned control car. Background technique [0002] In recent years, with the changes in the way of social life, various logistics companies undertake the distribution of more and more items. The main workflow of traditional logistics companies is: after the logistics arrives at the destination city, the courier delivery staff will manually deliver to the final destination. However, as the volume of logistics business increases, the delivery time requirements become shorter and shorter, and the tasks undertaken by express delivery personnel become heavier and heavier. Increasing the number of express delivery personnel will increase the labor cost of logistics companies. In addition, the manua...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02G06K9/00
CPCG05D1/0221G06V20/58
Inventor 朱斐吴文伏玉琛周小科
Owner SUZHOU UNIV
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