Trajectory planning and inverse kinematics solution of Kuka robot using COA along with pick and place application

In this work, Coyote optimization algorithm (COA) is used for inverse kinematics optimization of a 7 degrees-of-freedom Kuka robot. The Denavit–Hartenberg (D–H) Convention approach is used to compute the forward kinematics of the robotic arm. The fitness functions based on sum of squares of distance and torque are employed to compute the optimized inverse kinematics solution using the COA. A comparative analysis has been conducted with other optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO) and Grey wolf optimization (GWO), artificial bee colony (ABC) optimization, and whale optimization algorithm (WOA) to evaluate the performance of the proposed approach. The experimental results show that the COA leads to least computation error of 3.59 × 10^-7 and computation time of 1.405 s as compared to GA, PSO, GWO, ABC, and WOA algorithms. Further, jerk being control input has a major impact on the efficiency of robotic arm. COA is employed to obtain the optimal joint parameters, such as joint velocity, joint acceleration, and joint jerk, respectively. This leads to a minimum jerk trajectory which contributes to the smooth movement of Kuka arm. The simulation of Kuka robotic arm for pick and place operations is performed in CoppeliaSim, which further justifies its usage for real-time applications.