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A beta salp swarm algorithm meta-heuristic for inverse kinematics and optimization

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Abstract

This paper first reviews heuristic-based and bio-inspired contributions in inverse kinematics. A new inverse kinematics solver is then proposed based on beta distributed Salp Swarm Algorithm called β-SSA. The proposed algorithm is an alternative of the SSA algorithm where leading salps are distributed based on the beta function, enabling a better control of their repartition on the search space. The \(\beta\)-SSA inverse kinematics solver is named IK-\(\beta\)-SSA and can be considered as a generic framework. It uses a generic formulation of a forward kinematic model of a robotic system to retrieve its inverse solution. Inverse solution consists in obtaining a possible and feasible joint motions allow the robotic system to achieve a specific position while satisfying intrinsic constraints such as joints positions/ velocities limitations or path limitations. The \(\beta\)-SSA algorithm is first tested on a set of test functions and compared to nominal SSA prior to be applied to solve the inverse kinematics problem of the industrial robotic arm, Kuka Kr05-arc. The proposed method shows very competitive results when compared to classical SSA, QPSO, Bi-PSO, K-ABC and FA. The experimental results based on simulations and a Wilcoxon non-parametric statistical tests evidently show that the \(IK-\beta\)-SSA performs better than classical SSA, QPSO, Bi-PSO, K-ABC and FA for a single point inverse kinematics solution using a generic 8 Dof arm and the Kr05 industrial robot. For the path planning, a circular path tracking was investigated using the Kr05 robot and confirmed also that the \(\beta\)-SSA performs better than classical SSA, QPSO, Bi-PSO, K-ABC and FA.

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Authors and Affiliations

  1. Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, Tunisia

    Nizar Rokbani & Mohamed Slim

  2. Reserach Group on Intelligent Machines, ReGim-Lab, National Engineering School of Sfax, University of Sfax, Sfax, Tunisia

    Nizar Rokbani & Adel M. Alimi

  3. Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia

    Seyedali Mirjalili

  4. Yonsei Frontier Lab, Yonsei University, Seoul, Korea

    Seyedali Mirjalili

  5. Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Adel M. Alimi

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  1. Nizar Rokbani
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  2. Seyedali Mirjalili
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  4. Adel M. Alimi
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Correspondence to Nizar Rokbani.

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Rokbani, N., Mirjalili, S., Slim, M. et al. A beta salp swarm algorithm meta-heuristic for inverse kinematics and optimization. Appl Intell 52, 10493–10518 (2022). https://doi.org/10.1007/s10489-021-02831-3

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