ccxu

Introduction AM-Traj: A waypoint-based trajectory generator. This project proposes a framework for large-scale waypoint-based trajectory generation, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the problem, we conduct alternating minimization between boundary conditions and time durations of trajectory pieces. Algebraic convenience of both sub-problems is leveraged to escape poor local minima and achieve the lowest time consumption. Moreover, based on polynomial theory, an extremely fast feasibility checker is designed for various kinds of constraints. By incorporating it into our alternating structure, a constrained minimization algorithm is constructed to optimize trajectories on the premise of feasibility. Benchmark evaluation shows that our algorithm outperforms state-of-the-art waypoint-based methods regarding efficiency, optimality, and scalability. The algorithm can be incorporated in a high-level waypoint planner, which can rapidly search over a three-dimensional space for aggressive autonomous flights. The capability of our algorithm is experimentally demonstrated by quadrotor fast flights in a limited space with dense obstacles. Large-Scale Trajectory Generation in Flight Corridors: For quadrotor trajectory planning, describing a polynomial trajectory through coefficients and end-derivatives both enjoy their own convenience in energy minimization. We name them double descriptions of polynomial trajectories. The transformation between them, causing most of the inefficiency and instability, is formally analyzed in this paper. Leveraging its analytic structure, we design a linear-complexity scheme for both jerk/snap minimization and parameter gradient evaluation, which possesses efficiency, stability, flexibility, and scalability. With the help of our scheme, generating an energy optimal (minimum snap) trajectory only costs […]

Introduction We present systematic solutions to some aerial applications with the integration of our algorithmic researches, including state estimation, onboard mapping, optimal trajectory generation and robust control. Here we introduce the aerial agile tracking as an instance. In Fast-Tracker,  we propose a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. The solution properly handles the challenging situations where the intent of the target and the dense environments are unknown to the UAV. Our work is divided into two parts: target motion prediction and tracking trajectory planning. In the target motion prediction method, we adopt polynomial regression based on the past target observations. Especially, Bernstein basis polynomial is used to enforce dynamical constraints in the regression method. The generated trajectory is extrapolated as the prediction of target future motion. In the tracking trajectory planner, we design a heuristic function for the kinodynamic searching method that considers both the current target observation and motion prediction. Afterwards, a flight corridor that consists of a sequence of connected free-space 3-D grids is formed based on the results of the path searching. The back-end optimizer then generates a spatial-temporal optimal safe trajectory within the flight corridor. What’s more, due to the occlusion of obstacles, the limited sensing range, and the uncertainty of the target’s intent, it is hard for the UAV always to locate the target. We design a strategy so that the UAV can re-locate the target as soon as possible. The proposed solution is […]