4 research outputs found
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
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 a motion-capture facility. FlightGoggles is capable of
simulating a virtual-reality environment around autonomous vehicle(s). While a
vehicle is in flight in the FlightGoggles virtual reality environment,
exteroceptive sensors are rendered synthetically in real time while all complex
extrinsic dynamics are generated organically through the 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.Comment: Initial version appeared at IROS 2019. Supplementary material can be
found at https://flightgoggles.mit.edu. Revision includes description of new
FlightGoggles features, such as a photogrammetric model of the MIT Stata
Center, new rendering settings, and a Python AP
Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness
This paper 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 (6DOF) flight
dynamics model, including aerodynamics equations. Using a global dynamics model
enables the generation of aerobatics trajectories that exploit the entire
flight envelope, enabling 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 aerobatics maneuvers, a time-optimal drone racing
trajectory, and an airshow-like aerobatic sequence for three tailsitter
aircraft.Comment: 14 pages, 17 figures, video of experiments available at
https://aera.mit.edu/projects/TailsitterAerobatic
Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers
We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning. </jats:p