Collision avoidance for automated guided vehicles using deep reinforcement learning - Qin, Yifan (Nanyang Technological University, 2020)
It is crucial yet challenging to develop an efficient collision avoidance policy for robots. While centralized collision avoidance methods for multi-robot systems exist and they are often more accurate and error-free, decentralized methods have the potential to reduce the prohibitive computation where each robot generates paths without observing other robots’ states. As the first step towards a decentralized multi-robot collision avoidance system, this project aims to implement Deep Reinforcement Learning in the collision avoidance simulation of a single robot. The robot scans the environment around it and is supposed to find its way in a pre- designed map with multiple obstacles and branches. Several algorithms are tested and discussed in this project including Q Learning, SARSA, Deep Q Network (DQN), Policy Gradient (PG), Actor Critic, Deep Determinist Policy Gradient (DDPG), Distributed Proximal Policy Optimization (DPPO). Thorough comparisons between DQN, DDPG and DPPO are presented in this project.