Search

Mobile robots must have the ability to guarantee safety for operation in a dynamic environment and close to other moving objects. There are many research had been conducted to make the robot safer by utilizing sensors and big data technology to make the mobile robot able to navigate autonomously and intelligently. One of the key elements in autonomous robots is communication between robots. In this paper, device-to-device (D2D) technology has been used to develop communication between robots. To establish the algorithm for D2D communications, radio frequency (RF) used as communication protocols that can perform D2D communication in real-time applications. The performance of D2D communication was then be assessed in terms of distance and latency. RF transceiver module has been mounted on the robot with Arduino to allow communication between mobile robot to other mobile robots in order to transfer data from robot's sensors to the other mobile robots. By utilizing the gathered information and data, the robot can assess its surroundings and predict the movement of other robots to avoid collisions between robots. The results show that the RF transceiver module is capable to send and receive data between two robots with latency up to 4.865s. It is envisaged that the proposed module can be very useful for developing D2D communication between robots to operate in dynamic environments. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) is a geophysical locating method that uses radio waves to capture images below the surface of the ground in a minimally invasive way. It also requires two main essential equipment which is a transmitter and a receiving antenna. To address the problem, this project proposed the hyperbolic recognition of different utility pipes of GPR images at different level of penetration depth. In this framework, the raw data of GPR images were firstly to be pre-processed. The grayscale images were cropped, resized, and enhanced to increase the contrast of the features of the image. Then, the pre-processed GPR images were extracted using the Histogram of Oriented Gradient (HOG) method with three different windows. The extracted HOG features were then used as input to the k-Nearest Neighbor classifier. A series of experiments has been conducted using 10-fold cross-validation technique for training and testing the GPR data. Based on the result obtained, it shows that at depth 20 cm the average accuracy is about 99.87%, whereas at depth 40 cm the average accuracy achieved 100%. Thus, the result shows that the extracted HOG features exhibit the significant information of hyperbolic signature of different pipe size with different depth of buried object. Therefore the results seem promising in recognizing the hyperbolic of utilities. [ABSTRACT FROM AUTHOR]