Design and Implementation of Fuzzy Based Person Following Mobile Robot

Abstract:

Tracking a person successfully and following robustly is a significant ability that requires to be overwhelmed by a service robot while it requires completing some human-related tasks. Such capability has desires, which cannot be met pleasingly using conventional numerical process. Most remarkably, the robot has to stay at a certain safe distance as of the person that is being tracked and simultaneously be in motion in a smooth way which does not seem to be frightening to the person. In this research, consequently, a Fuzzy Inference System (FIS) is developed and used as a controller to provide decisions achieving smooth and safe person-following activities. The Fuzzy system is made to work in combination with a Optoelectronic (IR) sensor detection algorithm which acquire the position in using co-ordinate system and the velocity of the person is detected by using ultrasonic sensor and these are used to generate the Fuzzy Inference System distance and velocity information necessary for the control process. The simulation and result on this research established that even though the detection of IR is subject to minor noise and false negatives, the robot will achieve the smoothness and safety objectives while following its target. An example with a mobile robot tracking a person demonstrates the performance of our approach.

• Periodical: Applied Mechanics and Materials (Volume 151)
• Pages: 184-188
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.151.184
• Online since: January 2012
• Authors: Muhammad Mahbubur Rashid, Amir Akramin Shafie, Tarik Bin Alamgir, Ibrahim Jawad Alfar
• Keywords: Fuzzy Inference System (FIS), Fuzzy Membership Function, Person Detection, Person Following, Safety

References

[1] R. Gockley, J. Forlizzi, and R. Simmons, Natural person-following behavior for social robots, in Proceedings of the ACM/IEEE international conference on Human-robot interaction, 2007, p.17–24.

DOI: 10.1145/1228716.1228720

[2] J. Brookshire, Person Following Using Histograms of Oriented Gradients, International Journal of Social Robotics, vol. 2, no. 2, pp.137-146, Mar. (2010).

DOI: 10.1007/s12369-010-0046-y

[3] D. Gamrad and D. Soffker, Reduction of complexity for the analysis of human-machine-interaction, 2009 IEEE International Conference on Systems, Man and Cybernetics, no. October, pp.1263-1268, Oct. (2009).

DOI: 10.1109/icsmc.2009.5345910

[4] S. Thiel, D. Habe, and M. Block, Co-operative robot teams in a hospital environment, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, pp.843-847, Nov. (2009).

DOI: 10.1109/icicisys.2009.5358271

[5] T. Deyle, H. Nguyen, and M. Reynolds, RF vision: RFID receive signal strength indicator (RSSI) images for sensor fusion and mobile manipulation, Intelligent Robots and, pp.5553-5560, Oct. (2009).

DOI: 10.1109/iros.2009.5354047

[6] M. Kleinehagenbrock, S. Lang, and J. Fritsch, Person tracking with a mobile robot based on multi-modal anchoring, Robot and Human, no. September, pp.423-429, (2002).

DOI: 10.1109/roman.2002.1045659

[7] S. Shaker, J. J. Saade, and D. Asmar, Fuzzy Inference-Based Person-Following Robot, International Journal of Systems Applications, Engineering & Development, vol. 2, no. 1, p.29–34, (2008).

[8] T. Salter, K. Dautenhahn, and R. Boekhorst, Learning about natural human–robot interaction styles, Robotics and Autonomous Systems, vol. 54, no. 2, pp.127-134, Feb. (2006).

DOI: 10.1016/j.robot.2005.09.022

[9] D. Zhang, A Design and Simulation for Autonomous Robot's Obstacle Avoidance System Based on Fuzzy Control, Environments, pp.4-6, (2010).

[10] Information on http: /www. pololu. com/file/0J31/irb02a_guide2. pdf.