QP Chaser: Polynomial Trajectory Generation for Autonomous Aerial Tracking

Maintaining the visibility of the targets is one of the major objectives of aerial tracking applications. This paper proposes QP Chaser, a trajectory planning pipeline that can enhance the visibility of single- and dual-target in both static and dynamic environments. As the name suggests, the proposed planner generates a target-visible trajectory via quadratic programming problems. First, the predictor forecasts the reachable sets of moving objects with a sample-and-check strategy considering obstacles. Subsequently, the trajectory planner reinforces the visibility of targets with consideration of 1) path topology and 2) reachable sets of targets and obstacles. We define a target-visible region (TVR) with topology analysis of not only static obstacles but also dynamic obstacles, and it reflects reachable sets of moving targets and obstacles to maintain the whole body of the target within the camera image robustly and ceaselessly. The online performance of the proposed planner is validated in multiple scenarios, including high-fidelity simulations and real-world experiments.

Integrated Motion Planner for Real-time Aerial Videography with a Drone in a Dense Environment

This letter suggests an integrated approach for a drone (or multirotor) to perform an autonomous videography task in a 3-D obstacle environment by following a moving object. The proposed system includes 1) a target motion prediction module which can be applied to dense environments and 2) a hierarchical chasing planner based on a proposed metric for visibility. In the prediction module, we minimize observation error given that the target object itself does not collide with obstacles. The estimated future trajectory of target is obtained by covariant optimization. The other module, chasing planner, is in a bi-level structure composed of preplanner and smooth planner. In the first phase, we leverage a graph-search method to preplan a chasing corridor which incorporates safety and visibility of target during a time window. In the subsequent phase, we generate a smooth and dynamically feasible path within the corridor using quadratic programming (QP). We validate our approach with multiple complex scenarios and actual experiments. The source code can be found in https://github.com/icsl-Jeon/traj_gen_vis