Undergraduate Dissertation (University of Malta) 2020-2023 - 'Autonomous Drone Control using Reinforcement Learning'
This project aims to develop a system for autonomous drone control that focuses on the problem of drone obstacle avoidance. This is crucial for safe drone deployment in industries like search and rescue, package de- livery, and infrastructure inspections. The focus is on developing a rein- forcement learning-based solution for unmanned aerial vehicles (UAVs) to navigate through cluttered environments with static or moving obstacles. To achieve this, AirSim simulated drone physics, and the UnReal engine created the virtual environment. A depth sensor captured environmental data, used as input for the reinforcement learning algorithm. The algorithm learned from observations, with depth imagery being the most significant. A Convolutional Neural Network (CNN) processed the depth imagery to ex- tract relevant features. Additional inputs included velocity, distance from the goal, and action history. An Artificial Neural Network (ANN) processed the actions before combining them with the imagery. Four RL algorithms were trained in a static environment, and the best two were trained in a dynamic environment. These algorithims were: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), Proximal Policy Optimization (PPO), and Trust Region Policy Optimisation (TRPO). The best performance came from the DDQN algorithm, reaching the target goals 93% of the time in environments with static obstacles and achieving an average of 84.5% in environments with dynamic obstacles.
For the static environment, a closed corridor was constructed with a length of 61.3 m, width of 21.8 m, and height of 15 m. Obstacles in the form of columns with a diameter of 1m were placed every 10.8 m for a total of four levels. The drone had to navigate through the corridor, avoiding the obstacles and gaps between them to pass the test. The environment was designed to be randomized at each episode to achieve generalization. The vertical and horizontal columns could be shifted uniformly between -3m and +3m programmatically from their original location dictated by a parent column.
Static.Env.Short.mp4
For the dynamic environment, the same corridor dimensions were used, but instead of columns, cubes and squished cylinders of various sizes were used as obstacles. The obstacles were programmed to move from one point to another at a constant speed, taking 20 seconds in real time to reach their destination. Two approaches were taken to test generalization: one with randomized obstacle order and one with a familiar training environment but shuffled obstacle order.