Nonlinear trajectory generation for autonomous vehicles via parameterized maneuver classes

Chris Dever, Bernard Mettler, Eric Feron, Jovan Popović, Marc McConley

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


A technique is presented for creating continuously parameterized classes of feasible system trajectories. These classes, which are useful for higher-level vehicle motion planners, follow directly from a small collection of user-provided example motions. A dynamically feasible trajectory interpolation algorithm generates a continuous family of vehicle maneuvers across a range of boundary conditions while enforcing nonlinear system equations of motion as well as nonlinear equality and inequality constraints. The scheme is particularly useful for describing motions that deviate widely from the range of linearized dynamics and where satisfactory example motions may be found from off-line nonlinear programming solutions or motion capture of human-piloted flight. The interpolation algorithm is computationally efficient, making it a viable method for real-time maneuver synthesis, particularly when used in concert with a vehicle motion planner. Experimental application to a three-degree-of-freedom rotorcraft test bed demonstrates the essential features of system and trajectory modeling, maneuver example selection, maneuver class synthesis, and integration into a hybrid system path planner.

Original languageEnglish (US)
Pages (from-to)289-302
Number of pages14
JournalJournal of Guidance, Control, and Dynamics
Issue number2
StatePublished - 2006

Bibliographical note

Funding Information:
This research was funded under Draper Laboratory Internal Research and Development Project 13177, NASA Ames Research Center Project NAG 2-1552 for “Motion Planning for Agile Maneuvering Vehicles,” U.S. Air Force Research Laboratory Project F33615-01-C-1850 for “Safe Operation of Multi-Vehicle Systems,” and Navy–Office of Naval Research Project N00014-03-1-0171 for “Integrated Flight Management and Situational Awareness for Highly Maneuverable Autonomous Systems.” The authors thank Leena Singh, John Hauser, and Brent Appleby for their suggestions regarding algorithm development, as well as Tom Schouwenaars for his helpful discussions on higher-level motion planning and the incorporation of maneuver elements. Masha Ishutkina, Steve Hall, and the MIT Department of Aeronautics and Astronautics were extremely helpful in providing access to and support for the Quanser flights. The authors also thank the anonymous reviewers for their helpful feedback.


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