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

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.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Jun Xu,et al.  Cooperative Search and Exploration in Robotic Networks , 2013 .

[3]  An improved Particle Swarm Optimization algorithm , 2010, ICNC 2010.

[4]  Xingsheng Gu,et al.  A dynamic inertia weight particle swarm optimization algorithm , 2008 .

[5]  Anish Pandey,et al.  A review: On path planning strategies for navigation of mobile robot , 2019, Defence Technology.

[6]  M. Imran,et al.  An Overview of Particle Swarm Optimization Variants , 2013 .

[7]  S. Ying,et al.  Process of Microcellular Propellants with Adjustable Skin Thickness , 2013 .

[8]  B. K. Panigrahi,et al.  Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity , 2016 .

[9]  Jianhua Zhang,et al.  Robot path planning in uncertain environment using multi-objective particle swarm optimization , 2013, Neurocomputing.

[10]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  Yassine Hachaïchi,et al.  Democratic Inspired Particle Swarm Optimization for Multi-Robot Exploration Task , 2016 .

[12]  Kenya Jin'no,et al.  Analysis of dynamical characteristic of canonical deterministic PSO , 2010, IEEE Congress on Evolutionary Computation.

[13]  Akhtar Rasool,et al.  Heuristic and Meta-Heuristic Algorithms and Their Relevance to the Real World: A Survey , 2015 .

[14]  Yoram Koren,et al.  Potential field methods and their inherent limitations for mobile robot navigation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[15]  Koen V. Hindriks,et al.  Multi-robot Cooperative Pathfinding: A Decentralized Approach , 2014, IEA/AIE.

[16]  Sergio Nesmachnow,et al.  An overview of metaheuristics: accurate and efficient methods for optimisation , 2014, Int. J. Metaheuristics.

[17]  Alessandro Gasparetto,et al.  Optimal trajectory planning for industrial robots , 2010, Adv. Eng. Softw..

[18]  Ganesh K. Venayagamoorthy,et al.  Optimal PSO for collective robotic search applications , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[19]  Mohd. Nayab Zafar,et al.  Methodology for Path Planning and Optimization of Mobile Robots: A Review , 2018 .

[20]  Pratyusha Rakshit,et al.  Multi-robot path-planning using artificial bee colony optimization algorithm , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[21]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization , 2008, Scholarpedia.

[22]  Hua Zhu,et al.  Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm , 2016 .

[23]  Henk Nijmeijer,et al.  Mutual synchronization of robots via estimated state feedback: a cooperative approach , 2004, IEEE Transactions on Control Systems Technology.

[24]  Xiujuan Lei,et al.  A quarter century of particle swarm optimization , 2018, Complex & Intelligent Systems.

[25]  Shi Hongbo,et al.  Path planning for mobile robot based on particle swarm optimization , 2008, 2008 Chinese Control and Decision Conference.

[26]  Takefumi Hiraguri,et al.  Canonical deterministic particle swarm optimization to sustain global search , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[27]  Zhongyang Zheng,et al.  Research Advance in Swarm Robotics , 2013 .

[28]  Amit Konar,et al.  Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives , 2008, Advances of Computational Intelligence in Industrial Systems.

[29]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .