A New Classification Technique in Mobile Robot Navigation

This paper presents a novel pattern recognition algorithm that use weightless neural network (WNNs) technique.This technique plays a role of situation classifier to judge the situation around the mobile robot environment and makes control decision in mobile robot navigation. The WNNs technique is choosen due to significant advantages over conventional neural network, such as they can be easily implemented in hardware using standard RAM, faster in training phase and work with small resources. Using a simple classification algorithm, the similar data will be grouped with each other and it will be possible to attach similar data classes to specific local areas in the mobile robot environment. This strategy is demonstrated in simple mobile robot powered by low cost microcontrollers with 512 bytes of RAM and low cost sensors. Experimental result shows, when number of neuron increases the average environmental recognition ratehas risen from 87.6% to 98.5%.The WNNs technique allows the mobile robot to recognize many and different environmental patterns and avoid obstacles in real time. Moreover, by using proposed WNNstechnique mobile robot has successfully reached the goal in dynamic environment compare to fuzzy logic technique and logic function, capable of dealing with uncertainty in sensor reading, achieving good performance in performing control actions with 0.56% error rate in mobile robot speed

Three dimensional surface reconstruction of lower limb prosthetic model using infrared sensor array

This thesis addresses the development of a shape detector device using infrared sensor to reconstruct a three-dimensional image of an object. The threedimension image is produced based on the object surface using image processing technique. Conventionally, infrared sensors are used for detection of an obstacle and distance measurement to avoid collisions. However, it is not common to use infrared sensors to measure the size of an object. Hence, this research aims to investigate the feasibility of infrared sensors in measuring the object dimension for three-dimension image reconstruction. Experiments were executed to study the minimum distance range utilising GP2D120 infrared sensor. From the experiment, the distance between the sensor and object surface should be more than 5 cm. The scanning device consists of the infrared sensor array was placed in a black box with the object in the center. The scanning process required the object to turn 360 ° clockwise in an xy plane and the resolution for z-axis is 2 mm, in order to obtain data for the image reconstruction. Reference polygon shape models with various dimensions were used as scanning objects in the experiments. The device scans object diameter every 2 mm in thickness, 100 mm in height, and the total time required to collect data for each layer is 60 seconds. The reconstructed object accuracy is above 80 % based on the comparison between a solid and printed model dimension. Four different lower limb prosthetic models with different shapes were used as the object in the scanning experiments. The experimental findings show that the prosthetic shapes reconstructed with an average accuracy of 97 %. This system shows good reproducibility where the collected data using the infrared sensor device need further improvement so that it can be applied in medical field for orthotics and prosthetics purpose