QP Chaser: Polynomial Trajectory Generation for Autonomous Aerial Tracking

Maintaining the visibility of the target is one of the major objectives of aerial tracking missions. This paper proposes a target-visible trajectory planning pipeline using quadratic programming (QP). Our approach can handle various tracking settings, including 1) single- and dual-target following and 2) both static and dynamic environments, unlike other works that focus on a single specific setup. In contrast to other studies that fully trust the predicted trajectory of the target and consider only the visibility of the target's center, our pipeline considers error in target path prediction and the entire body of the target to maintain the target visibility robustly. First, a prediction module uses a sample-check strategy to quickly calculate the reachable areas of moving objects, which represent the areas their bodies can reach, considering obstacles. Subsequently, the planning module formulates a single QP problem, considering path homotopy, to generate a tracking trajectory that maximizes the visibility of the target's reachable area among obstacles. The performance of the planner is validated in multiple scenarios, through high-fidelity simulations and real-world experiments. Note to Practitioners - This paper proposes an aerial target tracking framework applicable to both single- and dual-target following missions. This paper proposes the prediction of the reachable area of moving objects and the generation of a target-visible trajectory, both of which are computed in real-time. Since the proposed planner considers the possible reach area of moving objects, the generated trajectory of the drone is robust to the prediction inaccuracy in terms of the target visibility. Our system can be utilized in crowded environments with multiple moving objects and extended to multiple-target scenarios. We extensively validate our system through several real-world experiments to show practicality.