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A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm

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Abstract

In this study, a quantum behaved particle swarm algorithm has used for inverse kinematic solution of a 7-degree-of-freedom serial manipulator and the results have been compared with other swarm techniques such as firefly algorithm (FA), particle swarm optimization (PSO) and artificial bee colony (ABC). Firstly, the DH parameters of the robot manipulator are created and transformation matrices are revealed. Afterward, the position equations are derived from these matrices. The position of the end effector of the robotic manipulator in the work space is estimated using Quantum PSO and other swarm algorithms. For this reason, a fitness function which name is Euclidian has been determined. This function calculates the difference between the actual position and the estimated position of the manipulator end effector. In this study, the algorithms have tested with two different scenarios. In the first scenario, values for a single position were obtained while values for a hundred different positions were obtained in the second scenario. In fact, the second scenario confirms the quality of the QPSO in the inverse kinematic solution by verifying the first scenario. According to the results obtained; Quantum behaved PSO has yielded results that are much more efficient than standard PSO, ABC and FA. The advantages of the improved algorithm are the short computation time, fewer iterations and the number of particles.

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Authors and Affiliations

  1. Computer Technology Department, Sakarya Vocational High School, Sakarya University, Sakarya, Turkey

    Serkan Dereli

  2. Electrical and Electronics Engineering, Faculty of Technology, Sakarya University, Sakarya, Turkey

    Raşit Köker

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  1. Serkan Dereli
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  2. Raşit Köker
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Correspondence to Serkan Dereli.

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Dereli, S., Köker, R. A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm. Artif Intell Rev 53, 949–964 (2020). https://doi.org/10.1007/s10462-019-09683-x

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