Trajectory Generation and Control for Precise Aggressive Maneuvers with Quadrotors
We study the problem of designing dynamically feasible trajectories and controllers that drive a quadrotor to a desired state in state space. We focus on the development of a family of trajectories defined as a sequence of segments, each with a controller parameterized by a goal state. Each controller is developed from the dynamic model of the robot and then iteratively refined through successive experimental trials to account for errors in the dynamic model and noise in the actuators and sensors. We show that this approach permits the development of trajectories and controllers enabling aggressive maneuvers such as flying through narrow, vertical gaps and perching on inverted surfaces with high precision and repeatability.
KeywordsPitch Angle Goal State Roll Angle Trajectory Generation Horizontal Opening
Unable to display preview. Download preview PDF.
- 1.Gillula, J.H., Huang, H., Vitus, M.P., Tomlin, C.J.: Design and analysis of hybrid systems, with applications to robotic aerial vehicles. In: Proc. of the Int. Symposium of Robotics Research, Lucerne, Switzerland (September 2009)Google Scholar
- 2.Gillula, J.H., Huang, H., Vitus, M.P., Tomlin, C.J.: Design of guaranteed safe maneuvers using reachable sets: Autonomous quadrotor aerobatics in theory and practice. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, Anchorage, AK, Anchorage, AK, May 2010, pp. 1649–1654 (2010)Google Scholar
- 3.Tedrake, R.: LQR-Trees: Feedback motion planning on sparse randomized trees. In: Proc. of Robotics: Science and Systems, Seattle, WA (June 2009)Google Scholar
- 4.Lupashin, S., Schollig, A., Sherback, M., D’Andrea, R.: A simple learning strategy for high-speed quadrocopter multi-flips. In: Lupashin, S. (ed.) Proc. of the IEEE Int. Conf. on Robotics and Automation, Anchorage, AK, May 2010, pp. 1642–1648 (2010)Google Scholar
- 5.Tang, J., Singh, A., Goehausen, N., Abbeel, P.: Parameterized maneuver learning for autonomous helicopter flight. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, Anchorage, AK, May 2010, pp. 1142–1148 (2010)Google Scholar
- 6.Abbeel, P.: Apprenticeship learning and reinforcement learning with application to robotic control. Ph.D. dissertation, Stanford University, Stanford, CA (August 2008)Google Scholar
- 8.Hoffmann, G., Waslander, S., Tomlin, C.: Quadrotor helicopter trajectory tracking control. In: AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, Hawaii (April 2008)Google Scholar
- 9.Vicon Motion Systems, Inc., http://www.vicon.com
- 10.Robot Operating System (ROS), http://www.ros.org
- 11.ROS-Matlab Bridge, http://github.com/nmichael/ipc-bridge
- 12.Ascending Technologies, GmbH, http://www.asctec.de