This article proposes a novel algorithm for aerobatic trajectory generation for a vertical take-off and landing (VTOL) tailsitter flying wing aircraft. The algorithm differs from existing approaches for fixed-wing trajectory generation, as it considers a realistic six-degree-of-freedom (6-DOF) flight dynamics model, including aerodynamic equations. Using a global dynamics model enables the generation of aerobatics trajectories that exploit the entire flight envelope, allowing agile maneuvering through the stall regime, sideways uncoordinated flight, inverted flight, etc. The method uses the differential flatness property of the global tailsitter flying wing dynamics, which is derived in this work. By performing snap minimization in the differentially flat output space, a computationally efficient algorithm, suitable for online motion planning, is obtained. The algorithm is demonstrated in extensive flight experiments encompassing six aerobatic maneuvers, a time-optimal drone racing trajectory, and an airshowlike aerobatic sequence for three tailsitter aircraft.

In this paper, we present a motion planning framework for multi-modal vehicle dynamics. Our proposed algorithm employs transcription of the optimization objective function, vehicle dynamics, and state and control constraints into sparse factor graphs, which—combined with mode transition constraints—constitute a composite pose graph. By formulating the multi-modal motion planning problem in composite pose graph form, we enable utilization of efficient techniques for optimization on sparse graphs, such as those widely applied in dual estimation problems, e.g., simultaneous localization and mapping (SLAM). The resulting motion planning algorithm optimizes the multi-modal trajectory, including the location of mode transitions, and is guided by the pose graph optimization process to eliminate unnecessary transitions, enabling efficient discovery of optimized mode sequences from rough initial guesses. We demonstrate multi-modal trajectory optimization in both simulation and real-world experiments for vehicles with various dynamics models, such as an airplane with taxi and flight modes, and a vertical take-off and landing (VTOL) fixed-wing aircraft that transitions between hover and horizontal flight modes.

FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it provides the ability to combine (i) synthetic exteroceptive measurements generated in silico in real time and (ii) vehicle dynamics and proprioceptive measurements generated in motio by vehicle(s) in flight in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous vehicle(s) in flight. While a vehicle is in flight in the Flight-Goggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex dynamics are generated organically through natural interactions of the vehicle. The FlightGoggles framework allows for researchers to accelerate development by circumventing the need to estimate complex and hard-to-model interactions such as aerodynamics, motor mechanics, battery electrochemistry, and behavior of other agents. The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, e.g., fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection. FlightGoggles has been utilized as the main test for selecting nine teams that will advance in the AlphaPilot autonomous drone racing challenge. We survey approaches and results from the top AlphaPilot teams, which may be of independent interest. FlightGoggles is distributed as open-source software along with the photorealistic graphics assets for several simulation environments, under the MIT license at http://flightgoggles.mit.edu.

The development of fast, agile micro Unmanned Aerial Vehicles (UAVs) has been limited by (i) on-board computing hardware restrictions, (ii) the lack of sophisticated vision-based perception and vision-in-the-loop control algorithms, and (iii) the absence of development environments where such systems and algorithms can be rapidly and easily designed, implemented, and validated. Here, we first present a new micro UAV platform that integrates high-rate cameras, inertial sensors, and an NVIDIA Jetson Tegra X1 system-on-chip compute module that boasts 256 GPU cores. The UAV mechanics and electronics were designed and built in house, and are described in detail. Second, we present a novel “virtual reality” development environment, in which photorealistically-rendered synthetic on-board camera images are generated in real time while the UAV is in flight. This development environment allows us to rapidly prototype computing and sensing hardware as well as perception and control algorithms, using real physics, real interoceptive sensor data (e.g., from the on-board inertial measurement unit), and synthetic exteroceptive sensor data (e.g., from synthetic cameras). Third, we demonstrate repeated agile maneuvering with closed-loop vision-based perception and control algorithms, which we have developed using this environment.

We consider pursuit-evasion games in which the pursuer is tasked with intercepting the evader using only partial measurements. Motivated by the utilization of visual sensing on board the pursuer, we focus on the case when only bearing measurements are available to the pursuer. The resulting partially-observable interception problem is computationally challenging, and the separation principle does not hold in general. In this paper, we identify a set of maneuvers that improve observability, and we propose an algorithm that utilizes these maneuvers to move the pursuer so that the expected payoff of the differential game is maximized. The algorithm uses in-the-loop uncertainty propagation based on linear covariance analysis to assess the effect of the maneuvers. We evaluate the resulting guidance law in experiments involving a quadcopter in flight representing the pursuer, and a simulated evader.