Optimal trajectory planning of 6 DOF manipulator using meta- heuristics integrated with artificial potential field theory
Ahmed H. Shahein, Mohammed Ghazy, Atef A. Ata · Alexandria Engineering Journal · 2025
In the last decades the Meta-heuristics and evolution algorithms were used in designing the optimal trajectory planning. These algorithms mimic the intelligence of the nature to optimize the trajectory planning under certain constraints. In this paper the Meta-Heuristics algorithms like Adaptive Particles Swarm Optimization APSO and Genetic Algorithms are used under certain constraints to make the robot interacts efficiently and effectively. Also obstacle avoidance will be investigated by driving the robot to acquire artificial intuition using potential field theory. In this study we managed to design a free collision optimum trajectory planning with minimum tracking error using APSO compared with GA. For the same degree of accuracy, the Adaptive Particle Swarm Optimization (APSO) provides faster convergence rate and reduces the computational time compared to GA. The integration of Artificial potential field theory enables us to know how to generate a free collision path to the end effector. The proposed APSO algorithm ensures that none of robot links intercepts with the obstacles during the course of motion, also offering excellent convergence and minimum error compared with other techniques.