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Robot communication control method, system and device based on federated reinforcement learning

A technology of reinforcement learning and robotics, applied in the direction of constraint-based CAD, based on specific mathematical models, computer-aided design, etc.

Active Publication Date: 2022-06-21
BEIJING UNIV OF POSTS & TELECOMM +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems of communication and path planning in the existing robot system, the present invention provides a robot communication control method, system and equipment based on federated reinforcement learning

Method used

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  • Robot communication control method, system and device based on federated reinforcement learning
  • Robot communication control method, system and device based on federated reinforcement learning
  • Robot communication control method, system and device based on federated reinforcement learning

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specific Embodiment approach 1

[0029] Specific implementation manner 1. Combination figure 1 and figure 2 Describe this embodiment, the robot communication control method based on federated reinforcement learning, the robot communication control method based on federated reinforcement learning described in this embodiment can be applied to any robot system that needs to perform path planning and transmit power allocation of access points , and, in the system, the reinforcement learning device at least includes: at least one robot and an access point.

[0030] It should be noted that the executive body of the robot communication control method based on federated reinforcement learning provided in this embodiment may be a control device, and the control device may be installed on the robot, or may be an independent device deployed outside the robot, The operation of the robot can be controlled by wireless signals; the control device can be a microcomputer, a processor, a mobile phone and other devices. In ...

specific Embodiment approach 2

[0094] Specific embodiment 2. Combination image 3 Describe this embodiment, a robot communication control system based on federated reinforcement learning, the system is applicable to the robot communication control method based on federated reinforcement learning described in Embodiment 1, and the system includes an information acquisition module, a scheme determination module and a resource allocation module;

[0095] an information acquisition module 310, configured to acquire the geographic location and current downlink channel gain of each robot in the reinforcement learning device at each moment;

[0096] The scheme determination module 320, based on the federated deep reinforcement learning method, determines the target resource allocation scheme of the current model; wherein, the target resource allocation scheme includes: indoor robot path planning and transmission power allocation of access points;

[0097] The resource allocation module 330 is used to control the r...

specific Embodiment approach 3

[0101] Specific embodiment three, combination Figure 4 Illustrating this embodiment, a control device includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404,

[0102] a memory 403 for storing computer programs;

[0103] The processor 401 is configured to implement the steps of the federated deep reinforcement learning-based indoor robot path planning and access point transmit power allocation method described in the first embodiment when executing the program stored in the memory 403 .

[0104] In this implementation manner, the communication bus mentioned by the control device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the li...

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Abstract

A robot communication control method, system and device based on federated reinforcement learning, relating to the fields of path planning technology and wireless communication resource allocation. To solve the problem of communication and path planning in the existing robot system, the method steps include: at the beginning of each aggregation cycle, each robot replaces the local network model parameters with the newly received global network model parameters, and in the aggregation cycle, each robot uses the local network model parameters. The network performs intensive learning training and updates the local network model parameters. Before the end of the aggregation period, each robot uploads the latest network model parameters to the access point; the access point performs global aggregation on all new local network model parameters to obtain new global model parameters, and Send the new global model parameters to the corresponding robot. The invention accelerates the convergence speed of the network, improves the long-term throughput of the system, has good robustness to the change of the number of robots, and can reduce the communication energy consumption of the robots and protect the privacy of the robots.

Description

technical field [0001] The invention relates to the field of path planning and wireless communication resource allocation, in particular to a method, system and device for robot communication control based on federated reinforcement learning. Background technique [0002] The explosive development of the Internet of Things has accelerated the large-scale application of intelligent robots in industrial control and home automation. In order to better provide new services in robotic systems, the system often requires a large number of communication, computing and data resources, which may require local devices to be acquired externally. To alleviate local hardware requirements, wireless systems must provide services with wide connectivity, low latency, and high data rates, and communication issues may limit the further development of multi-robot networks. Therefore, it is necessary to consider both communication and path planning problems in robotic systems. [0003] Non-orth...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20G06N7/00G06F111/02G06F111/04G06F111/08G06F119/10
CPCG06F30/20G06F2111/04G06F2111/02G06F2111/08G06F2119/10G06N7/01
Inventor 田辉罗如瑜倪万里陈志广
Owner BEIJING UNIV OF POSTS & TELECOMM
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