Trajectory Planning Using Artificial Potential Fields with Metaheuristics

The use of industrial robots has grown over the years, making production systems increasingly efficient. Within this context, some limitations appear that can delay the productive process causing damages to the production. These limitations are robot stops, for example. Stops can be caused by various factors, such as accidents, collisions of manipulator robots with operators or other equipment. The main contribution of this research is to improve the Artificial Potential Field (APF) using Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) by optimizing the APF parameters in collision avoidance. We present as results: the trajectories generated by a planar manipulator robot; the position errors between the final position and the last position of the generated trajectories; and the computational cost of the PSO, GA and DE algorithms to find the parameters of the APF algorithm.