Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this article, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce two perception-aware planning approaches to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We release our implementation as an open-source package1for the community.

Gradient-based trajectory optimization (GTO) has gained wide popularity for quadrotor trajectory replanning. However, it suffers from local minima, which is not only fatal to safety but also unfavorable for smooth navigation. In this paper, we propose a replanning method based on GTO addressing this issue systematically. A path-guided optimization (PGO) approach is devised to tackle infeasible local minima, which improves the replanning success rate significantly. A topological path searching algorithm is developed to capture a collection of distinct useful paths in 3-D environments, each of which then guides an independent trajectory optimization. It activates a more comprehensive exploration of the solution space and output superior replanned trajectories. Benchmark evaluation shows that our method outplays state-of-the-art methods regarding replanning success rate and optimality. Challenging experiments of aggressive autonomous flight are presented to demonstrate the robustness of our method. We will release our implementation as an open-source package1.

In this article, we propose a complete and robust system for the aggressive flight of autonomous quadrotors. The proposed system is built upon on the classical teach-and-repeat framework, which is widely adopted in infrastructure inspection, aerial transportation, and search-and-rescue. For these applications, a human's intention is essential for deciding the topological structure of the flight trajectory of the drone. However, poor teaching trajectories and changing environments prevent a simple teach-and-repeat system from being applied flexibly and robustly. In this article, instead of commanding the drone to precisely follow a teaching trajectory, we propose a method to automatically convert a human-piloted trajectory, which can be arbitrarily jerky, to a topologically equivalent one. The generated trajectory is guaranteed to be smooth, safe, and dynamically feasible, with a human preferable aggressiveness. Also, to avoid unmapped or moving obstacles during flights, a fast local perception method and a sliding-windowed replanning method are integrated into our system, to generate safe and dynamically feasible local trajectories onboard. We name our system as teach-repeat-replan. It can capture users' intention of a flight mission, convert an arbitrarily jerky teaching path to a smooth repeating trajectory, and generate safe local replans to avoid unexpected collisions. The proposed planning system is integrated into a complete autonomous quadrotor with global and local perception and localization submodules. Our system is validated by performing aggressive flights in challenging indoor/outdoor environments. We release all components in our quadrotor system as open-source ros packages.

Drones are enabling new forms of cinematography. Aerial filming via drones in action scenes is difficult because it requires users to understand the dynamic scenarios and operate the drone and camera simultaneously. Existing systems allow the user to manually specify the shots and guide the drone to capture footage, while none of them employ aesthetic objectives to automate aerial filming in action scenes. Meanwhile, these drone cinematography systems depend on the external motion capture systems to perceive the human action, which is limited to the indoor environment. In this paper, we propose an Autonomous CinemaTography system “ACT” on the drone platform to address the above the challenges. To our knowledge, this is the first drone camera system which can autonomously capture cinematic shots of action scenes based on limb movements in both indoor and outdoor environments. Our system includes the following novelties. First, we propose an efficient method to extract 3D skeleton points via a stereo camera. Second, we design a real-time dynamical camera planning strategy that fulfills the aesthetic objectives for filming and respects the physical limits of a drone. At the system level, we integrate cameras and GPUs into the limited space of a drone and demonstrate the feasibility of running the entire cinematography system onboard in real-time. Experimental results in both simulation and real-world scenarios demonstrate that our cinematography system “ACT” can capture more expressive video footage of human action than that of a state-of-the-art drone camera system.

In this paper, we present a framework to do optimal time allocation for quadrotor trajectory generation. Using this method, we can generate minimum-time piecewise polynomial trajectories for quadrotor flights. We decouple the quadrotor trajectory generation problem into two folds. Firstly we generate a smooth and safe curve which is parameterized by a virtual variable. This curve named spatial trajectory is independent of time and has fixed spatial properties. Then a mapping function which decides how the quadrotor moves along the spatial trajectory respecting kinodynamic limits is found by minimizing total trajectory time. The mapping function maps the virtual variable to time is named temporal trajectory. We formulate the minimum-time temporal trajectory generation problem as a convex program which can be efficiently solved. We show that the proposed method can corporate with various types of previous trajectory generation method to obtain the optimal time allocation. The proposed method is integrated into a customized light-weight quadrotor platform and is validated by presenting autonomous flights in indoor and outdoor environments. We release our code for time optimization as an open-source ros-package.