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Multi-robot cooperation and performance analysis with particle swarm optimization variants

  • 1211: AIoT Support and Applications with Multimedia
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Abstract

Cooperation and synchronization of multi-robots is a major concern in robotics research field. Two autonomous robots are assumed to carry a stick and called as the twin robots. Different types of Particle Swarm Optimization (PSO) are analyzed for stick carrying task and a brief review of extension and enhancement of PSO is done to identify the parameters used. Path planning of twin robot is done with variants of PSO. Performance of each variant-applied twin is evaluated based on several parameters. These parameters are execution time, number of steps, number of turns, path travelled and path deviated. Fitness value of each twin is calculated in each algorithm to obtain the next position along the solution path. All the algorithms are executed and the pixels are plotted to represent the twin’s trajectory and the performance of PSO variants compared with Artificial Bee Colony Optimization (ABCO) and differential Evolutionary (DE) algorithm. It is observed that PSO variants outperforms with respect to distance value.

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Abbreviations

PSO:

Particle Swarm Optimization

ABCO:

Artificial Bee Colony Optimization

DE:

Differential Evolutionary

A*:

Admissible search algorithm

AO*:

Ordered admissible search algorithm

ACO:

Ant Colony Optimization

GA:

Genetic Algorithm

BPSO:

Basic PSO

IPSO:

Improved PSO

RPSO:

Robotics PSO

DRPSO:

Democratic inspired robotics PSO

CPSO:

Canonical PSO

BPSOT:

BPSO applied Twin

IPSOT:

IPSO applied Twin

RPSOT:

RPSO applied Twin

DRPSOT:

DRPSO applied Twin

CPSOT:

CPSO applied Twin

v:

Velocity

x:

Position

k:

Time instance

α1, α2 :

Acceleration parameter assigned to 1

r1, r2 :

Random numbers in the range [0,1]

lbest:

Local best position

gbest:

Global best position

ωj :

Weight assigned to 0.7

αob :

Weight adjustment for obstacle avoidance

αcm :

Weight adjustment for communication maintenance

rob, rcm :

Random variables

xob :

Obstacle position

xcm :

Solution position

pn(t):

Desired coordinate point

γ:

Position controlling parameter

t:

Time

ρ:

Eigen value

σ:

Damping factor

θ:

Rotation angle

c:

No. of rotation

p:

Rotational cost

dcg :

Distance from goal position

F:

Fitness function

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Authors and Affiliations

  1. Department of Computer Science and Engineering, VSSUT-Burla, Sambalpur, India

    Bandita Sahu & Manas Ranjan Kabat

  2. Department of Information Technology, VSSUT-Burla, Sambalpur, India

    Pradipta Kumar Das

  3. Department of Computer Science and Engineering, GIET University, Gunupur, India

    Raghvendra Kumar

Authors
  1. Bandita Sahu
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  2. Pradipta Kumar Das
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  3. Manas Ranjan Kabat
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  4. Raghvendra Kumar
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Correspondence to Raghvendra Kumar.

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Sahu, B., Das, P.K., Kabat, M.R. et al. Multi-robot cooperation and performance analysis with particle swarm optimization variants. Multimed Tools Appl 81, 36907–36930 (2022). https://doi.org/10.1007/s11042-021-10986-x

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