Research for Multi-sensor Information Fusion Algorithm of Search and Rescue Robot Based on Embedded Control Network

Aiming at completing search task under disaster condition problems, an optimizing strategy based on multi-sensor information fusion is proposed in this paper. Firstly, search and rescue robot control system hardware circuit is designed; secondly, embedded system software design is realized; and then, a polymerization Kalman filtering model is proposed, it uses local Kalman filter weights scheduling principle to improve system fault-tolerant ability and overall fusion performance. What’s more, Adaboost algorithm realizes the multi-sensor information optimal fusion. Through simulation test experiment, the robot search traversal ability is verified under unstructured environment.

[1]  Hiroaki Yamaguchi,et al.  A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[2]  Gaurav S. Sukhatme,et al.  A portable, autonomous, urban reconnaissance robot , 2000, Robotics Auton. Syst..

[3]  Raymond van Ee,et al.  Unconstrained stereoscopic matching of lines , 2000, Vision Research.

[4]  Yoshiaki Shirai,et al.  Autonomous visual navigation of a mobile robot using a human-guided experience , 2002, Robotics Auton. Syst..

[5]  Peter I. Corke,et al.  Quantitative Evaluation of Matching Methods and Validity Measures for Stereo Vision , 2001, Int. J. Robotics Res..

[6]  Alain Pruski,et al.  An autonomous vehicle for people with motor disabilities , 2001, IEEE Robotics Autom. Mag..

[7]  Howie Choset,et al.  Coverage of Known Spaces: The Boustrophedon Cellular Decomposition , 2000, Auton. Robots.

[8]  Meng Wang,et al.  iBotGuard: an Internet-based Intelligent Robot security system using Invariant Face Recognition against intruder , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Ron Kimmel,et al.  Optimal Algorithm for Shape from Shading and Path Planning , 2001, Journal of Mathematical Imaging and Vision.

[10]  Howie Choset,et al.  Coverage for robotics – A survey of recent results , 2001, Annals of Mathematics and Artificial Intelligence.

[11]  Yong Gu.  Research on Optimization of Relief Supplies Distribution Aimed to Minimize Disaster Losses , 2011, J. Comput..

[12]  Thambipillai Srikanthan,et al.  High-Speed Environment Representation Scheme for Dynamic Path Planning , 2001, J. Intell. Robotic Syst..

[13]  Hobart R. Everett.  Robotic security systems , 2003 .

[14]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[15]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .