Flatness-Based Model Predictive Trajectory Planning for Cooperative Landing on Ground Vehicles

• Published in: 2021 IEEE Intelligent Vehicles Symposium (IV) pp. 1031 - 1036
• Main Authors: Hebisch, Christoph, Jackisch, Sven, Moormann, Dieter, Abel, Dirk
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.07.2021
• Subjects: Adaptation models; Computational modeling; Kinematics; Predictive models; Trajectory planning; Trajectory tracking; Unmanned aerial vehicles
• DOI: 10.1109/IV48863.2021.9575614

The autonomous landing of a fixed-wing unmanned aerial vehicle (UAV) on a moving unmanned ground vehicle (UGV) demands precise spatial synchronization of both vehicle systems. A promising strategy to achieve a successful landing in this heterogeneous rendezvous maneuver is the subdivision of the synchronization control task into a trajectory planner and underlying trajectory tracking controllers. For this purpose, a central trajectory planner, which is the subject of this paper, computes feasible trajectories for the UAV and UGV that converge towards each other. Current approaches mainly rely either on computationally expensive optimization problems or on potentially inaccurate linearization. In this paper, a new model predictive trajectory planning scheme based on the flatness property of kinematic models of the fixed-wing UAV and UGV is presented. By using vehicle models with this property, quadratic programs (QP) are formulated that can be solved efficiently in each time instance without linearization of the nonlinear vehicle models, which is a novel approach in this application. Crucial requirements for the trajectory planner are that it reduces the horizontal distance between the vehicles to below 0.5 m while maintaining safety constraints to allow safe landing, and a sampling rate of 40 Hz to allow rapid replanning in case of disturbances. To overcome the issue of input constraint transformation between the original and the flat models, different approximation methods are investigated. Simulation results with simple kinematic vehicle models and high-fidelity dynamic models using underlying trajectory tracking controllers and navigation filters are presented that demonstrate that the proposed trajectory planning method allows safe landing maneuvers with the considered vehicle setup.