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Problem statement:The purpose of any robotic is to perform tasks that a human would prefer not to do or hopefully do it with precision in order to avoid mistakes or when a human is out of duty due to fatigue or health reasons. The research into human detection into images has paid the way be aware of what is going on around the houses or buildings where a front-line security is needed 24 h a day. In this research a human detection security robot based on Gaussian distribution histogram was proposed. Approach: The proposed method consisted of three steps: (1) the RGB color space histogram was created by subdividing a color space into certain number of bins and then counted the number of pixels that each bin contains. (2) The created RGB histogram was converted into HSV color histogram using Gaussian distribution method. (3) The bell-shape curve of the Gaussian distribution was used to calculate the detection probability between the standard deviation. Results: Experimental evaluation had been tested on the images sequences where the experimental results revealed that the proposed method was less sensitive to changes in the scene achieving higher performance detection than traditional method of histogram creation and had been found to be robust. Conclusion: The results showed that the histogram based human detection resists to any changes in the image scenes.

Problem statement:Research into robot motion control offers research opportunities that will change scientists and engineers for year to come. Autonomous robots are increasingly evident in many aspects of industry and everyday life and a robust robot motion control can be used for homeland security and many consumer applications. This study discussed the adaptive fuzzy knowledge based controller for robot motion control in indoor and outdoor environment. Approach: The proposed method consisted of two components: the process monitor that detects changes in the process characteristics and the adaptation mechanism that used information passed to it by the process monitor to update the controller parameters. Results: Experimental evaluation had been done in both indoor and outdoor environment where the robot communicates with the base station through its Wireless fidelity antenna and the performance monitor used a set of five performance criteria to access the fuzzy knowledge based controller. Conclusion: The proposed method had been found to be robust.

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MemberThere exist several problems in the control of vehicle brake including the development of control logic for anti-lock braking system (ABS), base-braking and intelligent braking. Here we study the intelligent braking control where we seek to develop a controller that can ensure that the braking torque commended by the driver will be achieved. In particular, we develop, a new PID Fuzzy controller (PIDFC) based on parallel operation of PI Fuzzy and PD Fuzzy control. Two fuzzy rule bases are constructed by separating the linguistic control rule for PID Fuzzy control into two parts: The first part is e-∆e and the second part is ∆ 2 e-∆e respectively. Then two Fuzzy controls employing these rules bases individually are synthesized and run in parallel. The incremental control input is determined by taking weighted mean of the outputs of two Fuzzy controls. The result, which proves the merit of the proposed method are compared to those found in the previous research.