A simulation method and system for a production line mobile robot's collective recycling and warehousing

A technology of mobile robot and simulation method, which is applied in the field of simulation of aggregated recycling and warehousing of mobile robots in the production line, which can solve the problems that the control effect depends on the richness of training samples, cannot effectively cope with environmental diversity and various changes, and learn from experience.

Active Publication Date: 2022-06-21
UNIV OF JINAN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The inventors found that most of the existing control methods use the reinforcement learning control algorithm to make the agent learn the motion control strategy for the collective recovery and storage of mobile robots, but so far there is no simple and stable multi-agent cluster control At the same time, the existing methods based on deep reinforcement learning need to use self-explored samples for the agent to learn, and cannot learn from the environment independently and explore the unknown by itself. environment, which leads to its control effect heavily dependent on the richness of training samples, unable to effectively deal with the diversity and changes of the environment

Method used

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  • A simulation method and system for a production line mobile robot's collective recycling and warehousing
  • A simulation method and system for a production line mobile robot's collective recycling and warehousing
  • A simulation method and system for a production line mobile robot's collective recycling and warehousing

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Embodiment 1

[0042] The purpose of this embodiment is to provide a simulation method for a mobile robot in a production line to collect and collect into a warehouse.

[0043] A method for simulating a collection of mobile robots in a production line for collecting and returning to a warehouse, comprising:

[0044] Based on the scene information and the parameter information of the mobile robot, establish a kinematics model of the mobile robot for recycling;

[0045] Each mobile robot selects the storage location in the library as the target, uses the pre-trained improved depth deterministic policy gradient model to generate the optimal behavior strategy for each mobile robot, and realizes the recovery of the mobile robot through the control of force and speed;

[0046] Among them, the improved deep deterministic policy gradient model includes an actor network and a critic network, through the reward function mechanism based on the improved artificial potential energy function, the reward b...

Embodiment 2

[0087] The purpose of this embodiment is to provide a simulation system for a collection of mobile robots in a production line for collecting and returning to a warehouse.

[0088] A method for simulating a mobile robot in a production line with a collective return to warehouse, comprising:

[0089] a kinematic model construction unit, which is used to establish a kinematics model of the mobile robot that is returned to the warehouse based on the scene information and the parameter information of the mobile robot;

[0090] The path planning unit is used for each mobile robot to select the storage location in the library as the target, and uses the pre-trained improved depth deterministic policy gradient model to generate the optimal behavior strategy for each mobile robot, and realizes the mobile robot through the control of force and speed. recycling;

[0091] Among them, the improved deep deterministic policy gradient model includes actor network and critic network, through...

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Abstract

The present disclosure provides a simulation method and system for a production line mobile robot's aggregated recovery storage storage. The scheme realizes the intelligence of the deep deterministic strategy gradient algorithm by adding the improved artificial potential energy function mechanism to the depth deterministic strategy gradient algorithm. The reward function mechanism of the agent is designed. Through the reward mechanism based on the improved artificial potential energy function, the agent can learn the flocking actions with high rewards, and then realize the flocking effect of multiple agents; and through the deep deterministic Critics of the gradient algorithm The addition of specific agent local communication information in the neural network module enables the agent to better judge the surrounding environment, and enables them to learn a better cluster strategy to realize the movement of storage and recycling. .

Description

technical field [0001] The present disclosure belongs to the technical field of motion control of intelligent mobile robots, and in particular relates to a simulation method and system for a mobile robot in a production line with a collective return to warehouse. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] At this stage, with the rapid development of artificial intelligence technology, reinforcement learning algorithms are used to solve many complex problems in real life. A single-agent system is difficult to solve, and even if it can be solved, it will be limited in terms of speed and reliability, so using multiple agents to cooperate with each other can complete higher-level tasks. After multiple agents on the production line complete the task, it is necessary to realize the collective return of mobile robots on the production line...

Claims

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G05B17/02
CPCG05B17/02
Inventor张涵程金王琪琪王中华
OwnerUNIV OF JINAN