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4 results for “Ornithopter”
Adaptive Nonlinear Control For Perching of a Bioinspired Ornithopter
<p>This dataset contains the flight data recorded by the onboard flight computer for perching experiments. During the experiments nonlinear guidance and control laws are used for trajectory tracking. The filght computer is designed around the NanoPi Neo Air. A custom made PCB attached to the NanoPi, and acts as a carrier for an STM32 microcontroller. The microcontroller regulates the servos via a PWM signal. At the same time, the flight computer connects with an Optitrack motion capture system. The Optitrack emits data at 120 Hz to a local network using a VRPN protocol. This data is recorded by the flight computer, used for state estimation, and the control is executed at 100 Hz.</p>
Visual and inertial data for validation of gliding models of ornithopters
<p>This dataset contains data from different gliding flights with an ornithopter in low wind conditions. For each experiment, the inertial information is provided.</p> <p>Additionally, the flights have been recorded from three different points of view to track and triangulate its position. The videos are provided and the position of the camera has been determined using a Leica Total Station system with submillimeter accuracy. A sample of the 2D track of the ornithopter is provided for each video and experiment. The tracking along the three cameras are synchronized.</p> <p> </p> <p>------Camera Pose structure------</p> <p> </p> <p>Three cameras with four points: three to measure orientation and the last one the lens position. The last two points are the measured fall.<br> Camera 1 -> top left, bottom left and top right.<br> Camera 2 -> top left, top rigth and bottom right.<br> Camera 3 -> top left, bottom left and top right.<br> Then there are 14 rows. The pattern is: Point1, Point2, Point3 and Lens Position.</p> <p>------IMU structure------</p> <p>time, quaternion w, quaternion x, quaternion y, quaternion z, accelerometer x, accelerometer y, accelerometer z, Gyroscope x, Gyroscope y, Gyroscope z, magnetometer x ,magnetometer y ,magnetometer z<br> units: time->ms, accelerometer->g, gyroscope->ยบ/s</p> <p> </p>
Perception Sensor Dataset For Bioinspired Landing Trajectories Of An Ornithopter Robot
<p>The dataset contains the measurements captured by several onboard sensors during the landing maneuvers of an ornithopter robot. Each dataset contains a ROS bag file with the sensor measurements, a file with the bioinspired trajectory, a file with the events generated by the simulated event-based sensor, and a README file with the instructions to use the dataset.</p> <p>The bioinspired landing trajectories are computed using Tau Theory. Each landing trajectory test was performed in a simulated scenario. The object models of each scene can be found in the /model/meshes folder of each scene. There are two testing scenes: (i) a warehouse and (ii) a refinery. The file object_pose.csv includes the position and orientation of each object in the scene. The sensor measurements were saved in rosbag file that contains a topic for each sensor measurement. The dataset includes information from the following simulated sensors:</p> <ul> <li>Velodyne HDL-32E</li> <li>Sonar sensor with a range of 20 m</li> <li>IMU</li> <li>Frame based monocular camera</li> <li>Event camera</li> </ul>
How Ornithopters Can Perch Autonomously On A Branch
<p>Data used in the publication "How Ornithopters Can Perch Autonomously On A Branch", including both design and experimental data.</p>
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