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All Journal International Journal of Advances in Applied Sciences Journal of Robotics and Control (JRC)
Laghraib, Mohammed Khalil
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics

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Inverse Kinematics Optimization Using ACO, MOA, SPOA, and ALO: A Benchmark Study on Industrial Robot Arms El Mrabet, Aziz; Hihi, Hicham; Laghraib, Mohammed Khalil; Chahboun, Mbarek; Abouyaakoub, Mohcine; Ali, Ali Ait; Amalaoui, Aymane
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26809

Abstract

This study investigates the application of metaheuristic algorithms to solve the inverse kinematics (IK) problem in robotic manipulators, which is often challenging for high-degree-of-freedom systems. The research contribution is the comparative evaluation of four recent metaheuristic algorithms—Ant Colony Optimization, Mayfly Optimization Algorithm, Stochastic Paint Optimizer, and Ant Lion Optimizer—across different robot configurations. A kinematic analysis was conducted on three robotic arms: a 4-DOF SCARA, a 6-DOF ABB IRB 1600, and the dual-arm 15-DOF Motoman SDA20D/12L. For each manipulator, the end-effector pose was optimized by solving the IK problem using the selected algorithms. A total of 30 random target positions were tested within the operational space to ensure diversity in pose challenges; while not exhaustive, this sampling provides statistically informative trends. We evaluate each algorithm based on the number of optimal solutions obtained, the precision of the computed joint configurations, and execution time. The results indicate that the Mayfly Optimization Algorithm consistently delivered the highest precision with relatively low execution time across all robot types. In contrast, the Ant Lion Optimizer showed inconsistent performance in higher-DOF settings. Unlike traditional Jacobian-based or analytical IK methods, metaheuristics offer flexibility in handling complex, nonlinear systems without requiring gradient information. These findings provide practical insight for selecting suitable algorithms in real-world robotic applications.
Autonomous navigation system for a rover with robotic arm using convolutional neural networks El mrabet, Aziz; Hihi, Hicham; Laghraib, Mohammed Khalil; Chahboun, Mbarek; Amalaoui, Aymane
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp724-739

Abstract

The aim of this project is to design and develop an autonomous rover equipped with a KUKA robotic arm. This mobile vehicle will be able to move autonomously thanks to the use of machine learning techniques. It will also be able to detect and retrieve objects using the KUKA arm. The rover will feature Mecanum wheels for improved maneuverability and will be controlled by a Raspberry Pi 3 board, with machine learning algorithms implemented using TensorFlow and Python. The development process will follow the V-methodology. The use of such an autonomous rover and its manipulative capabilities opens the way to many practical applications, including sampling in dangerous or difficult-to-access environments, search and rescue operations in the event of natural disasters or industrial accidents, and inspection and maintenance of industrial or construction sites. The rover could also be used for educational purposes, enabling students to explore the concepts of robotics and artificial intelligence.
Co-Authors Abouyaakoub, Mohcine Ali, Ali Ait Amalaoui, Aymane Chahboun, Mbarek El Mrabet, Aziz Hihi, Hicham