Optimal protective region selection algorithm for patrol robots

This paper presents an optimal protection region selection algorithm based on color and texture. It employs a fuzzy approach to evaluate the similarity of color, in which morphological open operation and close operation are used to remove the noise in images. Moreover, the protection area is evaluated from the aspect of texture similarity and color similarity. With this approach, in patrol missions, robots could choose the best protective region-a region in which they are most unlikely to be recognized, so as to achieve the goal of hiding-themselves when approaching their targets. Two robots are applied to test the proposed algorithm in several different environments, namely a railway station, a warehouse and a campus. Test results show that this approach could select protective regions for patrol robots automatically, however, the method proposed can be influenced by the illumination and the view angle, needs further improvement.

[1]  Guangming Song,et al.  A surveillance robot with hopping capabilities for home security , 2009, IEEE Transactions on Consumer Electronics.

[2]  Achim J. Lilienthal,et al.  Has somethong changed here? Autonomous difference detection for security patrol robots , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  S. Merilaita,et al.  Animal camouflage: current issues and new perspectives , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[4]  Mandyam V. Srinivasan,et al.  Strategies for visual navigation, target detection and camouflage: inspirations from insect vision , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  Huosheng Hu,et al.  Toward Intelligent Security Robots: A Survey , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Heng Wang,et al.  Combination of RFID and vision for patrol robot navigation and localization , 2010, Proceedings of the 29th Chinese Control Conference.

[7]  Dipak Patil,et al.  Camouflage Technique Based Multifunctional Army Robot , 2015 .

[8]  Gary R. Bradski,et al.  Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library , 2016 .

[9]  J. Endler A Predator’s View of Animal Color Patterns , 1978 .

[10]  J. Zuanon,et al.  The almost invisible league: crypsis and association between minute fishes and shrimps as a possible defence against visually hunting predators , 2006 .

[11]  Andrew F. Laine,et al.  Visual textures, machine vision and animal camouflage. , 1992, Trends in ecology & evolution.

[12]  Man Li-chun Color recognition of license plates using fuzzy logic and learning approach , 2009 .

[13]  Yehezkel Yeshurun,et al.  Computer vision, camouflage breaking and countershading , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.