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PSO-based Multi-robot Formation Control

Author: ZhangMingZhong
Tutor: ZhangYaMing
School: Central South University
Course: Electronic Science and Technology
Keywords: multi-robot avoid robot avoid obstacle formation control particle swarm optimization
CLC: TP242
Type: Master's thesis
Year: 2011
Downloads: 58
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With the expansion of robot application, the demand to robot’s ability is more and more high. Multiple robots cooperatively complete more complex task that single robot can not complete. Formation control is a common and typical multi-robot coordination, and it is also the basis for multi-robot coordination. Formation control can greatly promote and facilitate multi-robot cooperative system research.Here we adopt behavior-based multi-robot formation control method, but there are two problems in the method of formation control, those are the design of effective basic behavior and the choice of better parameters of weight values.Firstly, for the problem of effective design of basic behavior, this thesis makes improvements on avoid static obstacles and avoid robot. We design a yaw angle that increases with the minimum distance between robot and obstacles or other robots decreases, which can smoothly avoid static obstacles or other robots. For the physical leader robot’s poor fault tolerance, we set a virtual leader at the geometric center of the formation, and guide the whole group’s moving.Secondly, for the problem of difficulty in selecting the better control values, we use particle swarm optimization algorithm to select a more appropriate control coefficients. However, due to high-dimensional opt-imization object is a function of multiple extreme points, the standard particle swarm optimization algorithm for high-dimensional target easily gets into the problem of prematurity and has slow convergence, so we proposed an improved adaptive particle swarm optimization, that is, according to the spatial distribution and evolution state of particles, we set the corresponding adaptive control parameters, and the particle which gets into a global optimal point takes non-uniformly jumping out and escaping strategy. The simulation shows that the improved algorithm has better performance.Finally, the improved oriented high-dimensional adaptive particle swarm optimization is applied to multi-robot formation control, and we get a better parameters. Then we do the simulation of multiple robots formation control on MATLAB platform. The result proved the feasibility of formation control algorithm and better performance.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Robotics > Robot
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