Safe flying for an UAV helicopter

Today small autonomous helicopters offer a low budget platform for aerial applications such as surveillance (both military and civil), land management and earth sciences. In this paper we introduce a prototype of autonomous aerial vehicle, the Helibot helicopter, specifically designed for applications in cooperative networks. Fundamental steps in the design process of an UAV are shown. We also present work in progress in the field of failure detection and a novel idea to the problem of failure recovery using a terrain vision system.

[1]  S. Longhi,et al.  Experimental Validation of a Real-Time Model-Based Sensor Fault Detection and Isolation System for Unmanned Ground Vehicles , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[2]  J. H. Kim.  A baro-altimeter augmented INS / GPS navigation system for an uninhabited aerial vehicle , 2003 .

[3]  Sauro Longhi,et al.  Improving the Robustness Properties of Robot Localization Procedures with Respect to Environment Features Uncertainties , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[4]  Mogens Blanke,et al.  Structural analysis - a case study of the rømer satellite , 2003 .

[5]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[6]  Timothy W. McLain,et al.  Decentralized Cooperative Aerial Surveillance Using Fixed-Wing Miniature UAVs , 2006, Proceedings of the IEEE.

[7]  Gianluca Ippoliti,et al.  A Multiple Models Approach for Adaptation and Learning in Mobile Robots Control , 2006, J. Intell. Robotic Syst..

[8]  Emanuele Frontoni,et al.  Appearance based robotics , 2006, IEEE Robotics & Automation Magazine.

[9]  A. Willsky,et al.  A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems , 1976 .

[10]  Martin Adams,et al.  A sequential failure detection technique and its application , 1976 .

[11]  Aníbal Ollero,et al.  Cooperative Fire Detection using Unmanned Aerial Vehicles , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Sauro Longhi,et al.  Model-Based Sensor Fault Detection and Isolation System for Unmanned Ground Vehicles: Theoretical Aspects (part I) , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[13]  Pavan Sikka,et al.  System design for a small autonomous helicopter , 2000 .

[14]  M. V. Iordache,et al.  Diagnosis and Fault-Tolerant Control , 2007, IEEE Transactions on Automatic Control.

[15]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[16]  Primo Zingaretti,et al.  Complete classification of raw LIDAR data and 3D reconstruction of buildings , 2006, Pattern Analysis and Applications.

[17]  Emanuele Frontoni,et al.  Adaptive and fast scale invariant feature extraction , 2007, VISAPP.

[18]  M. A. Mnich,et al.  Minimum-complexity helicopter simulation math model , 1988 .

[19]  Sauro Longhi,et al.  Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots , 1999, IEEE Trans. Robotics Autom..

[20]  Peter I. Corke,et al.  Low-cost vision-based AUV guidance system for reef navigation , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[21]  Vincent Cocquempot,et al.  Residual Generation for the Ship Benchmark Using Structural Approach , 1998 .

[22]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[23]  Emanuele Frontoni,et al.  Appearance-Based Robotics Robot Localization in Partially Explored Environments , 2006 .

[24]  S. Longhi,et al.  EXPERIMENTAL VALIDATION OF FASTSLAM ALGORITHM CHARACTERIZED BY A LINEAR FEATURES BASED MAP , 2005 .