ROAR: A Robust Autonomous Aerial Tracking System for Challenging Scenarios

Abstract

Autonomous tracking represents a significant advancement in the evolution of unmanned aerial vehicles (UAVs), offering applications in areas such as aerial photography and infrastructure inspection. Despite its potential, many autonomous tracking systems encounter challenges in maintaining consistent and reliable target tracking, particularly in complex and dynamic environments. This study presents a robust approach to autonomous tracking aimed at overcoming two primary challenges that often lead to tracking failures: target loss and poor tracking trajectory quality. To tackle these issues, a Markov chain-based motion prediction method is introduced to estimate the target's future position probabilities over time. Based on these predictions, a re-capture strategy is designed to enhance decision-making, ensuring effective recovery when the target exits the field of view (FOV). Additionally, the tracking process is modified by formulating cost functions that incorporate tracking distance and optimal yaw angle, while ensuring safety, dynamic feasibility, and operational constraints. Simulations and real-world experiments validate the proposed method, demonstrating stable and efficient tracking performance in challenging environments.