Georgios A. Demetriou

Control architectures for autonomous underwater vehicles

Autonomous underwater vehicles (AUVs) share common control problems with other air, land, and water unmanned vehicles. In addition to requiring high-dimensional and computationally intensive sensory data for real-time mission execution, power and communication limitations in an underwater environment make it more difficult to develop a control architecture for an AUV. In this article, the four types of control architectures being used for AUVs (hierarchical, heterarchical, subsumption, and hybrid architecture) are reviewed. A summary of 25 existing AUVs and a review of 11 AUV control architecture systems present a flavor of the state of the art in AUV technology. A new sensor-based embedded AUV control system architecture is also described and its implementation is discussed.

Mobile Robotics in Education and Research

Mobile robotics is a new field. Mobile robots range from the sophisticated space robots, to the military flying robots, to the lawn mower robots at our back yard. Mobile robotics is based on many engineering and science disciplines, from mechanical, electrical and electronics engineering to computer, cognitive and social sciences (Siegwart & Nourbakhsh, 2004). A mobile robot is an autonomous or remotely operated programmable mobile machine that is capable of moving in a specific environment. Mobile robots use sensors to perceive their environment and make decisions based on the information gained from the sensors. The autonomous nature of mobile robots is giving them an important part in our society. Mobile robots are everywhere, from military application to domestic applications. The first mobile robots as we know them today were developed during World War II by the Germans and they were the V1 and V2 flying bombs. In the 1950s W. Grey Walter developed Elmer and Elsie, two autonomous robots that were designed to explore their environment. Elmer and Elsie were able to move towards the light using light sensors, thus avoiding obstacles on their way. The evolution of mobile robots continued and in the 1970s Johns Hopkins University develops the “Beast”. The Beast used an ultrasound sensor to move around. During the same period the Stanford Cart line follower was developed by Stanford University. It was a mobile robot that was able to follow a white line, using a simple vision system. The processing was done off-board by a large mainframe. The most known mobile robot of the time was developed by the Stanford Research Institute and it was called Shakey. Shakey was the first mobile robot to be controlled by vision. It was able to recognize an object using vision, find its way to the object. Shakey, shown in Figure 1, had a camera, a rangefinder, bump sensors and a radio link. These robots had limitations due to the lack of processing power and the size of computers, and thus industrial robotics was still dominating the market and research. Industrial manipulators are attached to an off-board computer (controller) for their processing requirements and thus do not require an onboard computer for processing. Unlike industrial robots, mobile robots operate in dynamic and unknown environments and thus require many sensors (i.e. vision, sonar, laser, etc.) and therefore more processing power. Another important requirement of mobile robots is that their processing must be done onboard the moving robot and cannot be done off-board. The computer technology of the time was too bulky and too slow to meet the requirements of mobile robots. Also, sensor technology had to advance further before it could be used reliably on mobile robots.

Virtual environments for robotics education: an extensible object-oriented platform

The article discusses impediments that face researchers and academic institutions that try to implement training programs. The ability of virtual modeling and simulation (VM&S) systems to mitigate some problems is explained. A solution system, Virtual Robots (VROBO), is developed to demonstrate the effectiveness of the approach.

The Engino Robotics Platform (ERP) controller for education

The Engino Robotics Platform Controller, which is presented in this paper, is a control box intended for primary and early secondary education students. It is used to teach basic control, robotics and technology based courses. Along with the controller a series of external sensors have been developed that can be directly connected to the controller. The controller and the sensors allow students to build robots and other automated or interactive systems using the Engino components.

ERON: A flexible autonomous surface vessel

Aquatic unmanned robotic systems have gained popularity due to their abilities to perform a wide range of applications at low cost and no risk to human lives. This research investigates the development of the navigation system of “HPΩN”, an Autonomous Surface Vehicle (ASV). The PID control system composed of 3 Arduino UNO boards, a GPS, a Compass and 4 thrusters can navigate the 2,86 meter long and 0,7 meters wide vessel, at speeds of 3-4m/s with an accuracy of less than one meter. Due to its flexible architecture, available on-board payload space, and powerful thrusters, this platform can easily accommodate the integration of various sensors and scientific equipment for a wide range of applications involving water sampling, temperature and salinity measurements to border patrol and monitoring, independent of weather conditions.