Selected papers from the 2nd International Symposium on UAVs, Reno, U.S.A. June 8-10, 2009

1. Front Matter

   Pages i-vi

   PDF

2. Editorial

   • Kimon P. Valavanis

   Pages 1-7

3. Accurate Modeling and Robust Hovering Control for a Quad-rotor VTOL Aircraft

   • Jinhyun Kim, Min-Sung Kang, Sangdeok Park

   Pages 9-26

4. Modeling and System Identification of the muFly Micro Helicopter

   • Dario Schafroth, Christian Bermes, Samir Bouabdallah, Roland Siegwart

   Pages 27-47

5. Vision-based Position Control of a Two-rotor VTOL miniUAV

   • E. Rondon, S. Salazar, J. Escareno, R. Lozano

   Pages 49-64

6. A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance

   • C. Goerzen, Z. Kong, B. Mettler

   Pages 65-100

7. An Efficient Path Planning and Control Algorithm for RUAV’s in Unknown and Cluttered Environments

   • Kwangjin Yang, Seng Keat Gan, Salah Sukkarieh

   Pages 101-122

8. On the Generation of Trajectories for Multiple UAVs in Environments with Obstacles

   • Armando Alves Neto, Douglas G. Macharet, Mario F. M. Campos

   Pages 123-141

9. Integration of Path/Maneuver Planning in Complex Environments for Agile Maneuvering UCAVs

   • Emre Koyuncu, N. Kemal Ure, Gokhan Inalhan

   Pages 143-170

10. A Cost Effective Tracking System for Small Unmanned Aerial Systems

    • Michail Kontitsis, Kimon Valavanis

    Pages 171-191

11. Vision-Based Road-Following Using Proportional Navigation

    • Ryan S. Holt, Randal W. Beard

    Pages 193-216

12. A Vision-Based Automatic Landing Method for Fixed-Wing UAVs

    • Sungsik Huh, David Hyunchul Shim

    Pages 217-231

13. A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks

    • A. Cesetti, E. Frontoni, A. Mancini, P. Zingaretti, S. Longhi

    Pages 233-257

14. Autonomous Vision-Based Helicopter Flights Through Obstacle Gates

    • Franz Andert, Florian-M. Adolf, Lukas Goormann, Jörg S. Dittrich

    Pages 259-280

15. Exploring the Effect of Obscurants on Safe Landing Zone Identification

    • Keith W. Sevcik, Noah Kuntz, Paul Y. Oh

    Pages 281-295

16. Low-Cost Visual Tracking of a Landing Place and Hovering Flight Control with a Microcontroller

    • Karl E. Wenzel, Paul Rosset, Andreas Zell

    Pages 297-311

17. Landing and Perching on Vertical Surfaces with Microspines for Small Unmanned Air Vehicles

    • Alexis Lussier Desbiens, Mark R. Cutkosky

    Pages 313-327

18. Automating Human Thought Processes for a UAV Forced Landing

    • Pillar Eng, Luis Mejias, Xi Liu, Rodney Walker

    Pages 329-349

19. Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control

    • Konstantinos Dalamagkidis, Kimon P. Valavanis, Les A. Piegl

    Pages 351-369

20. Multimodal Interface Technologies for UAV Ground Control Stations

    • I. Maza, F. Caballero, R. Molina, N. Peña, A. Ollero

    Pages 371-391

In the last decade, signi?cant changes have occurred in the ?eld of vehicle motion planning, and for UAVs in particular. UAV motion planning is especially dif?cult due to several complexities not considered by earlier planning strategies: the - creased importance of differential constraints, atmospheric turbulence which makes it impossible to follow a pre-computed plan precisely, uncertainty in the vehicle state, and limited knowledge about the environment due to limited sensor capabilities. These differences have motivated the increased use of feedback and other control engineering techniques for motion planning. The lack of exact algorithms for these problems and dif?culty inherent in characterizing approximation algorithms makes it impractical to determine algorithm time complexity, completeness, and even soundness. This gap has not yet been addressed by statistical characterization of experimental performance of algorithms and benchmarking. Because of this overall lack of knowledge, it is dif?cult to design a guidance system, let alone choose the algorithm. Throughout this paper we keep in mind some of the general characteristics and requirements pertaining to UAVs. A UAV is typically modeled as having velocity and acceleration constraints (and potentially the higher-order differential constraints associated with the equations of motion), and the objective is to guide the vehicle towards a goal through an obstacle ?eld. A UAV guidance problem is typically characterized by a three-dimensional problem space, limited information about the environment, on-board sensors with limited range, speed and acceleration constraints, and uncertainty in vehicle state and sensor data.

• Model Predictive Control
• microcontroller
• model
• modeling
• simulation
• system
• system identification

• Department of Electrical and Computer Engineering, CMK 300 School of Engineering and Computer Science, University of Denver, Denver, USA

  Kimon P. Valavanis

• Dept. Electrical & Computer Engineering, Brigham Young University, Provo, USA

  Randal Beard

• Applied Engineering Technology Program, Drexel University, Philadelphia, USA

  Paul Oh

• Depto. Ingeniera Sistemas y Automática Camino de los Descubrimientos, Universidad de Sevilla Escuela Superior de Ingenieros, Sevilla, Spain

  Aníbal Ollero

• Dept. Computer Science & Engineering, University of South Florida, Tampa, USA

  Leslie A. Piegl

• Korea Advanced Institute of Science & Technology (KAIST), Department of Aerospace Engineering, Republic of South Korea

  Hyunchui Shim

Policies and ethics