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A Distributed Swarm Robot Collaborative Swarm Algorithm Based on Improved Gene Regulation Network

A gene regulation network and swarm robot technology, applied in the field of distributed swarm robot collaborative cluster control, can solve problems such as robot control communication burden

Active Publication Date: 2020-10-30
DONGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to overcome the disadvantages of the existing technology that can not simultaneously control the cluster formation of the robot with high accuracy, make the system calculation simple, and effectively reduce the communication burden, and provide a distributed group robot based on the improved gene regulation network Cooperative cluster control method

Method used

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  • A Distributed Swarm Robot Collaborative Swarm Algorithm Based on Improved Gene Regulation Network
  • A Distributed Swarm Robot Collaborative Swarm Algorithm Based on Improved Gene Regulation Network
  • A Distributed Swarm Robot Collaborative Swarm Algorithm Based on Improved Gene Regulation Network

Examples

Experimental program
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Effect test

Embodiment 1

[0130] According to the above model, set the total number of robots in the system to n=50, and the cluster formation distance d 1 = 1, the sensing range between robots r = 1.2, d 1 = 1.2, the distance range d of robot i’s obstacle avoidance response to surrounding robots and obstacles 2 =0.95, the value of δ is 0.01, and the Tr equation of the robot’s trajectory is as follows:

[0131]

[0132] For the NSGA II algorithm, the population size is set to 100, the crossover rate is 0.9, the SBX crossover distribution index is 20, the mutation probability is 0.2, the variation distribution index is 20, and the number of generations is set to 50 generations. Parameter range, a, l, m, c, k range from 1 to 100, b range from 1000 to 3000. Finally, the parameters obtained after NSGA II optimization are as follows:

[0133] Table 1 Model parameter values

[0134] a l b m c k 5 0.9 2590 10 165 45

[0135] The model is simulated by Matlab. In the experimen...

Embodiment 2

[0137] According to the above-mentioned model, some robots may fail to stop working during the traveling process. The situation is simulated by Matlab. The parameter setting is the same as that of Example 1, and the trajectory setting is also the same as that of Example 1. For example, Figure 9 As shown, start the normal cluster of robots. During the running process, 5 robots are randomly selected to make them stop moving, such as Figure 10 As shown, the symbol of the faulty robot changes from "o" to "*" after it stops moving. Figure 11 to Figure 13 It is shown that the rest of the robots can still self-organize and rebuild into a rhombus grid for clustering without collision, and the system has good robustness.

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Abstract

The invention provides a distributed group robot cooperative clustering algorithm based on an improved gene regulation network. Through embedding a diamond mesh distribution equation and a trajectoryfollowing equation in a gene regulation network model based on the Turing reaction diffusion mechanism, the moving vector speed of each robot is controlled, thus each robot whose initial state is in random distribution always gathers at a preset cluster trajectory position at a time t, the robots are arranged in a self-organized way to be in a diamond mesh distribution and can avoid obstacles in adynamic environment and repair formation by themselves. Parameter values in the improved gene regulation network are given by an NSGA II optimization algorithm. The distributed group robot cooperative clustering algorithm has the advantages of low computational complexity and good expansibility, for any robot, only the collection of the location information of a neighboring robot is needed, so arequired communication range is small, and the communication burden is effectively reduced. In addition, if some robots fail in operation, a system can still work normally, and the algorithm has goodrobustness and a great application prospect.

Description

technical field [0001] The invention belongs to the field of group robot control, and relates to a distributed group robot cooperative cluster control method based on an improved gene regulation network. Background technique [0002] With the development and maturity of mobile robot technology, the application and demand of human beings for robots are also increasing day by day. As a new type of production tool, robots have shown great advantages in reducing labor intensity, improving productivity, changing production modes, and liberating people from dangerous, harsh or heavy working environments. [0003] The swarm robot cluster refers to a large number of robots moving in formation according to a certain formation mode. Research on swarm robot swarm system has practical significance. On the one hand, it has broad application prospects and engineering value; on the other hand, it is a way to understand biological complexity. The common features of these systems are: indi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02G06F30/18G06F30/27
CPCG05D1/0287G05D1/0289
Inventor 郝矿荣李贞蔡欣唐雪嵩丁永生
Owner DONGHUA UNIV
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