UC San Diego

This thesis focuses on solving the problem of motion planning for autonomous mobile robots using reinforcement learning. In recent years, Reinforcement Learning (RL) has shown remarkable potential in training autonomous agents to navigate complex environments. In the context of autonomous ground vehicles, one of the fundamental challenges is achieving seamless navigation amidst obstacles. Our work delves into the development and evaluation of a reinforcement learning agent, implemented using the Proximal Policy Optimization (PPO) algorithm, guiding an Ackermann drive car towards a goal while avoiding obstacles. The key innovation lies in the fusion of car states and depth images using various neural network architectures to extract features that drives the RL agent's decision-making process. The problem of motion planning also comprises of an optimality criteria of the planned path depending on the state space. Our work considers the time to navigate to the goal as an optimality criteria, which is being controlled by hyperparameters of the reward function formulated. Finally, we implement our formulation on a f1-tenth car with close to no tuning during sim2real transfer. We deploy the network trained in the simulation as-is on the vehicle. Stabilization of the policy was taken by care regularizing the trained network carried out by targetting the lipschitz continuity of the trained network.