WSNs and on-Board Visual Fuzzy Servoing on Blimp Robot for Tracking Purposes

The aerial robots represent an interested and rich area of research because they are very useful to perform complex tasks such as localization and tracking targets. To develop blimp system that is appropriate in diversity scenarios, an intelligent control with high autonomy degree is required. Therefore, we design blimp robot based on embedded system; then, we present several fuzzy sets models that should deal with autonomous, navigation and visual tracking problems. These models are empirically designed by combining the possibilities distributions theory with fuzzy logic. Thus, this paper addresses the problem of tracking robots in parallel with achieving the cooperative behavior based on computer vision system and artificial intelligent control to improve the efficiency of such system. In addition, considering use of wireless sensor networks for estimation multi-targets locations and the distances between them is presented.

[1]  Alexandra Moutinho,et al.  Stability and Robustness Analysis of the AURORA Airship Control System using Dynamic Inversion , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[2]  Sumetee kesorn.  Visual Navigation for Mobile Robots: a Survey , 2012 .

[3]  H. Roth,et al.  Design blimp robot based on embedded system and software architecture with high level communication and fuzzy logic , 2013, 2013 9th International Symposium on Mechatronics and its Applications (ISMA).

[4]  Chansik Park,et al.  Extended Kalman Filter for wireless LAN based indoor positioning , 2008, Decis. Support Syst..

[5]  Jaegeol Yim,et al.  Improvement of Kalman filters for WLAN based indoor tracking , 2010, Expert Syst. Appl..

[6]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[7]  Nils Melzer,et al.  Human Rights Implications of the Usage of Drones and Unmanned Robots in Warfare , 2013 .

[8]  Chen Chwan-Hsen,et al.  Real Time Multi-target Visual Tracking based on Velocity Segmentation Technique , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[9]  Tao Xu,et al.  Onboard controlling system design of unmanned airship , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[10]  James R. Bergen,et al.  Visual odometry for ground vehicle applications , 2006, J. Field Robotics.

[11]  Yu Zhang,et al.  Modelling Radar Tracking Features and Low Observability Motion Planning for UCAV , 2012, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Hubert Roth,et al.  Visual Fuzzy Control for Blimp Robot to Follow 3D Aerial Object , 2014 .

[14]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[15]  Josué Jr. Guimarães Ramos,et al.  Robotic Airships for Exploration of Planetary Bodies with an Atmosphere: Autonomy Challenges , 2003, Auton. Robots.

[16]  D. Capson,et al.  Biomimetic measurement of optical flow and centroid for visual-servo control of hover flight , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[17]  Sridha Sridharan,et al.  Improved Simultaneous Computation of Motion Detection and Optical Flow for Object Tracking , 2009, 2009 Digital Image Computing: Techniques and Applications.

[18]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[19]  Cheng-Ming Huang,et al.  Target motion compensation with optical flow clustering during visual tracking , 2014, Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control.

[20]  Hubert Roth,et al.  Blimp Based on Embedded Computer Vision and Fuzzy Control for Following Ground Vehicles , 2013, TA.

[21]  Avideh Zakhor,et al.  Indoor localization and visualization using a human-operated backpack system , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[22]  Masayuki Inaba,et al.  Visual navigation using view-sequenced route representation , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[23]  Farrah Wong,et al.  Soft computing control system of an unmanned airship , 2010, 2010 1st International Conference on Energy, Power and Control (EPC-IQ).

[24]  Mohammad A. Al-Jarrah,et al.  Cooperative OFDM for semi distributed detection in wireless sensor networks , 2014 .

[25]  Christof Röhrig,et al.  Localization of Sensor Nodes in a Wireless Sensor Network Using the nanoLOC TRX Transceiver , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[26]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Hubert Roth,et al.  Developed Blimp Robot Based On Ultrasonic Sensors Using Possibilities Distribution and Fuzzy Logic , 2013 .

[28]  J. Little,et al.  Inverse perspective mapping simplifies optical flow computation and obstacle detection , 2004, Biological Cybernetics.

[29]  Christof Röhrig,et al.  Tracking of transport vehicles for warehouse management using a wireless sensor network , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Shinji Ozawa,et al.  Visual Navigation of an Autonomous Vehicle Using White Line Recognition , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Zong-jian Lin,et al.  Low Altitude Photogrammetry of Unmanned Airship , 2009, 2009 International Conference on Information Engineering and Computer Science.

[32]  Hubert Roth,et al.  Multiple 3D Target Tracking in Binary Wireless Sensor Network , 2015 .