Selected papers from the 2nd international symposium on UAVs, Reno, Nevada, U.S.A., June 8-10, 2009
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書誌事項
Selected papers from the 2nd international symposium on UAVs, Reno, Nevada, U.S.A., June 8-10, 2009
Springer, c2010
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Selected papers from the second international symposium on UAVs
Selected papers from the 2nd international symposium on unmanned aerial vehicles
Selected papers from the second international symposium on unmanned aerial vehicles
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注記
"Previously published in Journal of Intelligent and Robotic Systems, Volume 57, Issues 1-4 2010."--T.p
内容説明・目次
内容説明
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.
目次
- Provisional Table of Contents, August 2009: A: UAS Modeling, Control and Identification: J. Kim, M. S. Kang, S. Park: Accurate Modeling and Robust Hovering Control for a Quad-rotor VTOL Aircraft
- D. Schafroth, C. Bermes, S. Bouabdallah, R. Siegwart: Modeling and System Identification of the muFly Micro Helicopter
- E. Rondon, S. Salazar, J. Escareno, R. Lozano, A. Malo-Tamayo: Vision-Based Position Control of a Two-Rotor VTOL mini UAV
- B: UAS Navigation, Path Planning and Tracking: C. Goerzen, Z. Kong, B. Mettler: A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance
- K. Yang, S. K. Gan, S. Sukkarieh: An Efficient Path Planning and Control Algorithm for an RUAV in an Unknown and Cluttered Environment
- A. A. Neto, D. G. Macharet, M. F. M. Campos: On the Generation of Trajectories for Multiple UAVs in Environments with Obstacles
- E. Koyuncu, N. K. Ure, G. Inalhan: Integration of Path/Maneuver Planning in Complex Environments for Agile Maneuvering UCAVs
- M. Kontitsis, K. Valavanis: A Cost-Effective Tracking System for Small Unmanned Aerial Systems
- C: UAS Vision and Vision-Based Systems
- R. S. Holt, R. W. Beard: Vision-Based Road-Following Using Proportional Navigation
- S. Huh, D. H. Shim: A Vision-based Automatic Landing Method for Fixed-wing UAVs
- A. Cesetti, E. Frontoni, A. Mancini, P. Zingaretti, S. Longhi: A Vision-based Guidance System for UAVs Navigation and Safe Landing using Natural Landmarks
- F. Andert, F. M. Adolf, L. Goormann, J. S. Dittrich: Autonomous Vision-Based Helicopter Flights through Obstacle Gates
- D: Landing and Forced Landing
- K. W. Sevcik, N. Kuntz, P. Y. Oh: Exploring the Effect of Obscurants on Safe Landing Zone Identification
- K. E. Wenzel, P. Rosset, A. Zell: Low-Cost Visual Tracking of a Landing Place and Hovering Flight Control with a Microcontroller
- A. L. Desbiens, M. Cutkosky: Landing and Perching on Vertical Surfaces with Microspines for Small Unmanned Air Vehicles
- P. Eng, L.Mejias, X. Liu: Automating Human Thought Processes for a UAV Forced Landing
- K. Dalamagkidis, K. Valavanis, L. Piegl: Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control
- E: Simulation Platforms and Testbeds: I. Maza, F. Caballero, R. Molina, N. Pena, A. Ollero: Multimodal Interface Technologies for UAV Ground Control Stations: A Comparative Analysis
- R. Garcia, L. Barnes: Multi-UAV Simulator Utilizing X-Plane
- J. Saunders, R. Beard: UAV Flight Simulation with Hardware-in-the-loop Testing and Vision Generation
- F: UAS Applications: I. Maza, K. Kondak, M. Bernard, A. Ollero: Multi-UAV Cooperation and Control for Load Transportation and Deployment
- S. Erhard, K. E. Wenzel, A. Zell: Flyphone: Visual Self-Localization Using a Mobile Phone as Onboard Image Processor on a Quadrocopter
- A. H. Goktogan, S. Sukkarieh, M. Bryson, J. Randle, T. Lupton, C. Hung: Using Rotary-Wing Unmanned Aerial Vehicles for Aquatic Weed Surveillance and Management
- J. T. Hing, K. W. Sevcik, P. Y. Oh: Development and Evaluation of a Chase View for UAV Operations in Cluttered Environments
- M. Aksugur, G. Inalhan: Design Methodology of a Hybrid Propulsion Driven Electric Powered Miniature Tailsitter Unmanned Aerial Vehicle.
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