Analysis of the Inverse Kinematics and Trajectory Planning Applied in a Classic Collaborative Industrial Robotic Manipulator

In this work, the approaches of genetic algorithms (GA) and artificial neural networks (ANN) are compared to solve the inverse kinematics applied in a robotic manipulator. The method with the best result in the comparison is then used to act in conjunction with another concept of robotics which is collaborative robotics, responsible for increasing the safety of both the manipulator and the human being when an object and/or person appears in the trajectory of the manipulator. The classic concept of inverse kinematics is related to the relatively new concept of collaborative robotics through trajectory planning, which in this work used the fifth-order polynomial due to its ability to control position, speed, and acceleration. According to the results obtained, the best method in the comparison for the solution of the inverse kinematics was that of artificial neural networks because it has the shortest response time and the most robust results.