Appraisal of Continuous Use of Public Debt on the Nation’s Growth and Development

The paper examines the continuous use of public debt in Nigeria, the effect of growth and development on the nation. The nature of public debt was discussed before considering reasons most nations including Nigeria go for public debt. The Ricardo Theory of public debt was the theoretical framework for the paper. The relationship between public debt and national development was also explored. Based on that conclusion was reached. Keywords: External debt; domestic debt; growth; development DOI: 10.7176/JRDM/78-03 Publication date:August 31st 202

Vision-Based Close Formation Flight of Unmanned Aerial Vehicles (UAVs)

Since cost of unmanned aircraft vehicles have decreased recently due to technological advancement, there has been a growing interest in developing and implementing systems for close formation missions. Our research objective is to investigate and implement low-cost vision-based tracking algorithms for such a flight formation. For the first technical objective (TO), we are developing an algorithm for vision-based tracking using a Raspberry-Pi hardware. For the second TO, we assembled a quadcopter to be equipped with a camera module and a calibrated flight control computer. In addition, the research team has performed flight testing to obtain video data of a flying marked quadcopter as a reference for developing the tracking algorithm. The final TO is to test-fly two quadcopters in close formation using vision-based tracking algorithm. Ultimately, this research will provide a reliable platform to further investigate formation flight capabilities, and to extrapolate the technology to a wide range of applications

Intelligent Robotic Navigation Through Simultaneous Localization and Mapping with Convolutional Neural Networks

A study is presented on intelligent robotic navigation through simultaneous localization and mapping (SLAM) enhanced with convolutional neural networks (CNNs). The study included re-training a pre-trained CNN network for object detection, recognition and depth estimation in a laboratory setting, implementing a feature-based monocular SLAM algorithm (ORB-SLAM) within robot operating system (ROS) framework and integrating both the re-trained CNN and ORB-SLAM to intelligently guide a robot during navigation to reach target objects while avoiding obstacles. The visual SLAM (ORB-SLAM) enhanced with CNN for object detection, recognition and depth estimation was adapted and implemented in real-time. A Kobuki Turtlebot with Xbox 360 camera along with its on-board laptop (CPU based) was selected as the mobile robotic platform for the implementation within ROS framework. The proposed system successfully combined the capabilities of ORB-SLAM with CNN for real-time autonomous navigation of the robot in an enclosed environment. The power of edge computing with graphics processing unit (GPU) based hardware platform Jetson TX2 along with open-source software library TensorFlow suitable for implementation of deep learning architectures including CNN were utilized within ROS for real-time operation. The effectiveness of the system is illustrated through case studies that required the robot to avoid obstacles while locating designated objects within the map including a maze in a laboratory setting. In each case, the robot was able to plan a path towards the target objects using the map it saved from the ORB-SLAM-CNN implementation. The map was continuously updated accommodating changes in the environment while the robot was navigating towards the target objects. Details of algorithms developed, hardware and software support, real-time implementation, and results of different case studies are presented along with recommendations for future work