Agile drone flight through narrow gaps with onboard sensing and computing

Published on Sep 27, 2016

We present a method to let a quadrotor autonomously pass through narrow gaps using only onboard sensing and computing. We estimate the full state by fusing gap detections from a single onboard camera with an IMU. We generate a trajectory that considers geometric, dynamic, and perception constraints. During the approach maneuver, the quadrotor actively controls its orientation such that it always faces the gap to allow robust state estimation. During the traverse through the gap, the quadrotor maximizes the distance from the edges of the gap to minimize the risk of collision. The traverse maneuver does not require any visual feedback, which is not available during this phase. The trajectory is continuously replanned during its execution to cope with the varying uncertainty of the state estimate. After the traverse is complete, the quadrotor automatically recovers and locks to a hovering position. We successfully evaluated our approach with gap orientations of up to 45 degrees. Our vehicle weighs 830 grams and has a thrust-to-weight ratio of 2.5. The vehicle reaches speeds of up to 3 meters per second and angular velocities of up to 400 degrees per second, with accelerations of up to 1.5 g. We can pass through gaps 1.5 times the size of the quadrotor, with only 10 centimeters of tolerance. Our method does not require any prior knowledge about the position and the orientation of the gap. No external infrastructure, such as a motion-capture system, is needed. This is the first time that such an aggressive maneuver through narrow gaps has been done by fusing gap detection from a single onboard camera and IMU.

Research webpage:
rpg.ifi.uzh.ch/aggressive_flight.html

Robotics and Perception Group, University of Zurich, 2016
Robotics and Perception Group


FPV drone flight through narrow gaps

Published on Sep 29, 2016

We challenged two Swiss drone-racing pilots to demonstrate FPV flight through narrow gaps. It turned out not to be that easy.. but after some a few attempts they managed quite well! How would a completely autonomous drone perform when challenged on the same task?