Localization and perching maneuver tracking for a morphing UAV

Autonomous vehicle control requires knowledge of the vehicle's states that often can only be estimated using sensor measurements. Several sensor types are typically used for the estimation process and each type often has its own sensing characteristics. This paper considers a novel morphing unmanned aerial vehicle (UAV) that is capable of changing its configuration in-flight and using aerodynamic forces to perform a perching maneuver. This maneuver could allow the UAV to perform planted landings and enable the vehicle to land in new locations, such as on building rooftops. However, this task requires the system controller to have accurate knowledge of vehicle states, especially with respect to the landing location. Visual sensors are required for identification of the landing site and to provide the relative positioning information that is critical for autonomous landings when uncertainty exists in the landing coordinates. Such information is unavailable from either a global navigation satellite system (GNSS) or inertial measurements to sufficient accuracy. The key objective of this research is to develop a foundation for the control of an aircraft that is highly nonlinear. This paper investigates the use of a set of linear motion models to represent the full range of nonlinear dynamics for an aircraft performing a perching maneuver. Simulation data are presented and their results discussed.

[1]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[2]  Gaurav S. Sukhatme,et al.  Towards vision-based safe landing for an autonomous helicopter , 2002, Robotics Auton. Syst..

[3]  Mark D. Guynn,et al.  Evolution of a Mars Airplane Concept for the ARES Mars Scout Mission , 2003 .

[4]  Gaurav S. Sukhatme,et al.  Visually guided landing of an unmanned aerial vehicle , 2003, IEEE Trans. Robotics Autom..

[5]  Robert F. Stengel,et al.  Optimal Control and Estimation , 1994 .

[6]  Ephrahim Garcia,et al.  Optimization of Perching Maneuvers Through Vehicle Morphing , 2008 .

[7]  Ephrahim Garcia,et al.  Longitudinal dynamics of a perching aircraft , 2006 .

[8]  Ephrahim Garcia,et al.  Aerodynamic Modeling of Morphing Wings Using an Extended Lifting-Line Analysis , 2007 .

[9]  Ernst D. Dickmanns,et al.  Autonomous landing of airplanes by dynamic machine vision , 1992, [1992] Proceedings IEEE Workshop on Applications of Computer Vision.

[10]  Louis V. Schmidt,et al.  Introduction to Aircraft Flight Dynamics , 1998 .

[11]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .

[12]  Michael Athans,et al.  The stochastic control of the F-8C aircraft using a multiple model adaptive control (MMAC) method--Part I: Equilibrium flight , 1977 .

[13]  Eric N. Johnson,et al.  Vision-Only Aircraft Flight Control Methods and Test Results , 2004 .

[14]  Timothy W. McLain,et al.  Vision-Based Landing of Fixed-Wing Miniature Air Vehicles , 2007 .

[15]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[16]  Amir Averbuch,et al.  Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .