Path Planning Collision Avoidance using Reinforcement Learning

Josias  G. Batista; Felipe  J. S. Vasconcelos; Kaio  M. Ramos; Darielson  A. Souza; José  L. N. Silva

doi:10.48011/asba.v2i1.1597

Josias G. Batista Instituto Federal de Educação Ciência e Tecnologia do Ceará
Felipe J. S. Vasconcelos Universidade Federal do Ceará
Kaio M. Ramos Universidade Federal do Ceará
Darielson A. Souza Universidade Federal do Ceará
José L. N. Silva Instituto Federal de Educação Ciência e Tecnologia do Ceará

DOI: https://doi.org/10.48011/asba.v2i1.1597

Keywords: Path planning, Collision avoidance, Reinforcement learning, Robotic manipulator, Trajectory generation

Abstract

Industrial robots have grown over the years making production systems more and more efficient, requiring the need for efficient trajectory generation algorithms that optimize and, if possible, generate collision-free trajectories without interrupting the production process. In this work is presented the use of Reinforcement Learning (RL), based on the Q-Learning algorithm, in the trajectory generation of a robotic manipulator and also a comparison of its use with and without constraints of the manipulator kinematics, in order to generate collisionfree trajectories. The results of the simulations are presented with respect to the efficiency of the algorithm and its use in trajectory generation, a comparison of the computational cost for the use of constraints is also presented.