Design and Implementation of a Collision Avoidance Module in Dynamic Environment with Deep Reinforcement Learning on Arash Social Robot

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Norouzi, Mostafa | 2022

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54941 (08)
University: Sharif University of Technology
Department: Mechanical Engineering
Advisor(s): Meghdari, Ali; Taheri, Alireza; Soleymani, Mahdieh
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 contrastive representation learning method was also used to improve the performance of the trained neural network. The results obtained from the evaluation of neural networks on 500 tests in the simulation environment show that contrastive representation learning improves the robot routing performance in social environments
Keywords:
Deep Reinforcement Learning ; Reinforcement Learning ; Collision Avoidance ; Contrastive Learning ; Robot Pathplanning ; Contrastive Representation Learning ; Robot Path Planning in Dynamic Environment ; Arash Robot