Learning Stabilization Control of Quadrotor in Near-Ground Setting Using Reinforcement Learning

With the development of intelligent systems, the popularity of using micro aerial vehicles (MAV) increases significantly in the fields of rescue, photography, security, agriculture, and warfare. New modern solutions of machine learning like ChatGPT that are fine-tuned using reinforcement learning (RL) provides evidence of new trends in seeking general artificial intelligence. RL has already been proven to work as a flight controller for MAV performing better than Proportional Integral Derivative (PID)-based solutions. However, using negative Euclidean distance to the target point as the reward function is sufficient in obstacle-free spaces, e.g. in the air, but fails in special cases, e.g. when training near the ground. In this work, we address this issue by proposing a new reward function with early termination. It not only allows to successfully train Proximal Policy Optimization (PPO) algorithm to stabilize the quadrotor in the near-ground setting, but also achieves lower Euclidean distance error compared to the baseline setup.