Path Planning Collision Avoidance Using Reinforcement Learning

• Autor(a) principal: Batista, Josias Guimarães
• Data de Publicação: 2021
• Outros Autores: Dias, Emerson Veras Aragão, Vasconcelos, Felipe José de Sousa, Ramos, Kaio Martins, Souza, Darielson Araújo de, Silva, José Leonardo Nunes da, Lopes, João Luiz Dallamuta, Holzman, Henrique Ajuz
• Tipo de documento: Capítulo de livro
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/65139
• topic: Planejamento de caminho; Prevenção de colisão; Aprendizagem por reforço; Manipulador robótico; Geração de trajetória; Path planning; Collision avoidance; Reinforcement learning; Robotic manipulator; Trajectory generation

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 collision free 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.