Motion states extraction with optical flow for rat-robot automatic navigation

The real-time acquisition of precise motion states is significant and difficult for bio-robot automatic navigation. In this paper, we propose a real-time video-tracking algorithm to extract motion states of rat-robots in complex environment using optical flow. The rat-robot's motion states, including location, speed and motion trend, are acquired accurately in real time. Compared with the traditional methods based on single frame image, our algorithm using consecutive frames provides more exact and rich motion information for the automatic navigation of bio-robots. The video of the manual navigation experiments on rat-robots in eight-arm maze is applied to test this algorithm. The average computation time is 25.76 ms which is less than the speed of image acquisition. The results show that our method could extract the motion states with good performance of accuracy and time consumption.

[1]  Shennan A. Weiss,et al.  Rat navigation guided by remote control , 2002 .

[2]  Yuichi Motai,et al.  A Real-Time Rodent Tracking System for Both Light and Dark Cycle Behavior Analysis , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[3]  C. Vorhees,et al.  A method for measuring locomotor behavior in rodents: contrast-sensitive computer-controlled video tracking activity assessment in rats. , 1992, Neurotoxicology and teratology.

[4]  Jake K. Aggarwal,et al.  On the computation of motion from sequences of images-A review , 1988, Proc. IEEE.

[5]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  C. Stiller,et al.  Estimating motion in image sequences , 1999, IEEE Signal Process. Mag..

[7]  Yuichi Motai,et al.  Video-Based Tracking and Incremental Learning Applied to Rodent Behavioral Activity Under Near-Infrared Illumination , 2007, IEEE Transactions on Instrumentation and Measurement.

[8]  Chao Sun,et al.  An Automatic Control System for Ratbot Navigation , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[9]  C. Stiller,et al.  Estimating Motion in Image Sequences A tutorial on modeling and computation of 2 D motion , 2022 .

[10]  Patrick Bouthemy,et al.  Computation and analysis of image motion: A synopsis of current problems and methods , 1996, International Journal of Computer Vision.

[11]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[12]  Hans-Hellmut Nagel,et al.  Optical Flow Estimation: Advances and Comparisons , 1994, ECCV.

[13]  Kui Yuan,et al.  A remote controlled multimode micro-stimulator for freely moving animals. , 2006, Sheng li xue bao : [Acta physiologica Sinica].

[14]  L P Noldus,et al.  EthoVision: A versatile video tracking system for automation of behavioral experiments , 2001, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[15]  Wang Yong.  A Telemetry Navigation System for Animal-robots , 2006 .

[16]  Peng Wang,et al.  A remote control training system for rat navigation in complicated environment , 2007 .

[17]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[18]  Isao Shimoyama,et al.  Locomotion control of a bio-robotic system via electric stimulation , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.