Motion planning for redundant robotic manipulators using a novel multi-group particle swarm optimization
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RESEARCH PAPER
Motion planning for redundant robotic manipulators using a novel multi‑group particle swarm optimization Zikai Feng1 · Lijia Chen1 · Chung‑Hao Chen2 · Mingguo Liu1 · Meng‑en Yuan1 Received: 19 February 2019 / Revised: 10 February 2020 / Accepted: 28 February 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Metaheuristic optimization algorithms are widely used in motion planning of redundant robotic manipulators. Existing methods may converge to a local minimum. In this paper, a new multi-group particle swarm optimization algorithm (PSOEL) is proposed to solve the motion planning of manipulators. PSOEL consists of one elite group and several child groups. The population is initialized with a pre-selection mechanism in which the members of the elite group are initialized with the bestperforming particles of the child groups. In the process of iteration, the elite group and the child groups evolve separately. When the elite group falls into a local optimum or is inferior to child groups for a certain time, an interaction mechanism is triggered. In the interaction mechanism, some of the best particles selected from the child groups will replace the bad particles of the elite group. With these mechanism of pre-selection and interaction, PSOEL can jump out of the local optimum and reach the global optimum or global suboptimum. Simulation results show that the proposed algorithm PSOEL is superior to the compared algorithms and converges toward the optimum. Keywords Multi-group PSO · The mechanism of pre-selection and interaction · Motion planning for redundant robotic manipulators
1 Introduction Recently, engineering design problems using metaheuristic optimization algorithms have been of particular interest to researchers because they offer safe and efficient solutions for human beings [1]. Evolutionary-based, nature-inspired and swarm-based methods are three kinds of the most important methods in metaheuristics [2]. The evolutionary algorithms are inspired by theory of natural selection, which often perform well. The commonly used algorithm for robotic manipulator is genetic algorithm (GA). In the work by Vijay and Jena [3], two control schemes for the robotic manipulator with two degrees of freedom are discussed. GA is used in the conventional Proportional Derivative (PD) and Proportional Integral Derivative (PID) controllers to develop three different control strategies. In the work by Jiang and Wang [4], * Lijia Chen [email protected] 1
School of Physics and Electronics, Henan University, Kaifeng, China
Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
2
a hybrid LM–GA algorithm was proposed to calibrate the camera system of a space manipulator to improve its locational accuracy. The camera system of a space manipulator is calibrated by hybrid LM–GA, which dynamically fuses the Levenberg–Marqurdt (LM) algorithm and GA together. Differential evolutionary (DE) algorithm has been used for engineering problems [5]. In t
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