Search results

The ROV ground control simulator (Fig. 1) used in this multi-sensory research consists of two workstations: pilot and SO. At the left workstation, the pilot controls ROV flight (via stick-and-throttle inputs as well as invoking auto-holds), manages subsystems, and handles external communications. From the right workstation, the SO is responsible for locating and identifying points of interest on the ground by controlling cameras mounted on the ROV. Each station has an upper and a head-level 17″ color CRT display, as well as two 10″ head-down color displays. The upper CRT of both stations displays a ‘God's Eye’ area map (fixed, north up) with overlaid symbology identifying current ROV location, flight waypoints, and current sensor footprint. The head-level CRT (i.e., “camera display”) displays simulated video imagery from cameras mounted on the ROV. Head-up display (HUD) symbology is overlaid on the pilot's camera display and sensor specific data are overlaid on the SO's camera display. The head-down displays present subsystem and communication information as well as command menus. The simulation is hosted on four dual-Pentium PCs. The control sticks are from Measurement Systems Inc. and the throttle assemblies were manufactured in-house.

Educational Robotics is a research field aimed at promoting an active engaging learning through the artifacts students create and the phenomena they simulate. In fact, designing, building, and programming a small robot, users discover and learn in a playful and joyful way. Moreover, the constructivist approach fosters the development of creative and critical skills, as well as problem-solving, communication skills, cooperation, and teamwork. This chapter presents the results of a research with university students, carried out at the Università della Calabria (Italy): an Educational Robotics laboratory has been integrated in a Cognitive Psychology Course in order to examine the kind of learning, workgroup retention, and engagement. Outcomes show that engaging experiences can remarkably enhance students’ learning efficiency and retention of the acquired materials. Moreover, a rich interaction can provide entertaining and appealing experiences capable of promoting learning and understanding.

In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.

Purpose – The purpose is to expand multimodal composition frameworks and practices to include tactile design and use of maker technologies, situated in a larger context of designing for equity and increasing access to picture books for children with visual impairments.

Design – As part of the Build a Better Book project, we designed workshops to engage students in composing tactile books enhanced with sound and Braille for young children with visual impairments. Education undergraduates in a children’s literature class crafted tactile retellings over a 2-session workshop, and high school students in an ELA class designed and fabricated 3D printed tactile books over several weeks.

Findings – Both pre-service candidates and high school students developed awareness of the importance of inclusive, equity-oriented design of picture books, and especially for children with visual impairments. They collaborated in teams, developing design skills manipulating texture, shape, size and spatial arrangement to express their tactile retellings and enhanced meaning with sound. The high school students had more opportunity to build technical and computational thinking through their use of Makey Makey, Scratch, and TinkerCad.

Practical Implications – Multimodal composition and making can be effectively integrated into pre-service candidates’ literacy education, as well as high school English Language Arts, to develop multimodal communication and inclusive design skills and values. Success depends on interdisciplinary expertise (e.g., children’s books, tactile design, making technologies, etc.), and sufficient access to physical and digital materials and tools.

Purpose: This chapter describes the design and implementation, at the computer hardware and software level, of the Greggg robot. Greggg is a scalable high performance, low cost hospitality robot constructed from off-the-shelf parts. Greggg has a robust architecture and acts as a tour guide on-campus, both indoors or outdoors. This research allows one to build a customized robot at a low cost, under U.S. $2,000, for accomplishing the desired hospitality tasks, and scale, and expand the capability of the robot as required.

Practical Implications: The practical implication of the research is the capability to build and program a robot for hospitality tasks. Greggg is a customizable robot capable of giving on-campus tours both indoors and outdoors. In its current architecture, Greggg can be trained to be a museum docent and give directions to visitors on-campus or at an airport and scaled up for other hospitality tasks using off-the-shelf components. Enhancing the robot by scaling it up and expanding it, in addition to testing it with a range of increasingly more difficult tasks using machine learning algorithms, is highly beneficial to advancing research on the use of robots in the hospitality sector. Greggg can also be used for Robot-as-a-service (Rass) applications.

Societal Implications: The economic implication of Greggg is the ease and low cost with which one, with minimal technology know-how, can construct an autonomous hospitality industry robot. This chapter details the hardware and software needed to build a low cost scalable and customizable autonomous robot for the hospitality industry without having to pay an exorbitant price.

Research/Limitations/Implications: This research allows one to build their own customized hospitality robot under U.S. $2,000. Given the cost of building the robot, it has limitations on the hospitality tasks it can perform. It can navigate on flat surfaces, has limited vision and speech processing capabilities and has a battery life not exceeding an hour. Furthermore, it does not have any robotic manipulators or tactile processing capabilities.

SD is defined as a failure to sense correctly the attitude, motion, and/or position of the aircraft with respect to the surface of the earth (Benson, 2003). The types of SD are generally thought to be “unrecognized” and “recognized” (Previc & Ercoline, 2004). Although a third type has been reported (incapacitating), this type seems irrelevant to UAV operations. Unrecognized SD occurs when the person at the controls is unaware that a change in the motion/attitude of the aircraft has taken place. The cause is often the result of a combination of sub-threshold motion and inattention. This type of SD is known to be the single most serious human factors reason for aircraft accidents today, accounting for roughly 90% of all known SD-related mishaps (Davenport, 2000). Recognized SD occurs when a noticeable conflict is created between the actual motion/attitude of the aircraft and any one of the common physiological sensory mechanisms (e.g., visual, vestibular, auditory, and tactile). Recognized SD is the most common type of SD, accounting for the remaining SD-related accidents.