Design and Implementation of a Collision Avoidance Module in Dynamic Environment with Deep Reinforcement Learning on Arash Social Robot, M.Sc. Thesis Sharif University of Technology ; Meghdari, Ali (Supervisor) ; Taheri, Alireza (Supervisor) ; Soleymani, Mahdieh (Co-Supervisor)
Abstract
Nowadays, one of the challenges in social robotics is to navigate the robot in social environments with moving elements such as humans. The purpose of this study is to navigate the Arash 2 social robot in a dynamic environment autonomously without encountering moving obstacles (humans). The Arash 2 robot was first simulated in the Gazebo simulator environment in this research. The simultaneous location and mapping (SLAM) technique was implemented on the robot using a lidar sensor to obtain an environment map. Then, using the deep reinforcement learning approach, the neural network developed in the simulation environment was trained and implemented on the robot in the real environment. The...
Cataloging briefDesign and Implementation of a Collision Avoidance Module in Dynamic Environment with Deep Reinforcement Learning on Arash Social Robot, M.Sc. Thesis Sharif University of Technology ; Meghdari, Ali (Supervisor) ; Taheri, Alireza (Supervisor) ; Soleymani, Mahdieh (Co-Supervisor)
Abstract
Nowadays, one of the challenges in social robotics is to navigate the robot in social environments with moving elements such as humans. The purpose of this study is to navigate the Arash 2 social robot in a dynamic environment autonomously without encountering moving obstacles (humans). The Arash 2 robot was first simulated in the Gazebo simulator environment in this research. The simultaneous location and mapping (SLAM) technique was implemented on the robot using a lidar sensor to obtain an environment map. Then, using the deep reinforcement learning approach, the neural network developed in the simulation environment was trained and implemented on the robot in the real environment. The...
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