We present a mobile pointing and input system for mobile phones with minimal hardware parts that allows users who wear an eyeglass display to view and interact with a large virtual image. To use the mobile phone as a pointing and input device, a fiducial marker-based tracking system developed for augmented reality is adapted for our system. The brightness and shape of active marker could be changed according to the state of operating environment and/or the user's intentions in order to increase the detection rate of the marker.

Typical robot development requires that hardware be mostly functional before significant software development begins. Utilizing virtual prototype of hardware and its environment can reduce development time and manufacturing cost. Virtual prototyping is, however, a highly challenging task requiring in-depth knowledge in many disciplines. Few of simulation tools developed to alleviate this difficulty fully manage the complexity caused by the fact that developer must design and manage form (physical shape), function, and behavior altogether incrementally. Also, such simulation tools often do not support co-development of the target software and its test environment from requirements analysis to implementation. In this paper, we present our experience of developing a virtual prototype of Samsung Home Robot (SHR) with Samsung Advanced Institute of Technology (SAIT) using ASADAL/OBJ. Virtual prototyping in ASADAL/OBJ enables incremental co-development of both target system and its test environment in an object-oriented way. Furthermore ASADAL/OBJ facilitates concurrent development of form, behavior, and function. These features increase productivity as well as confidence through incremental refinement and validation. We give a brief background on the ASADAL/OBJ framework, then illuminate our experience of developing a virtual prototype of SHR.

With the advances of robotics, computer science, and other related areas, home service robots attract much attention from both academia and industry. Home service robots present interesting technical challenges to the community in that they have a wide range of potential applications, such as home security, patient caring, cleaning, etc., and that the services provided by the robots in each application area are being defined as markets are formed and, therefore, they change constantly.Without architectural considerations to address these challenges, robot manufacturers often focus on developing technical components (e.g., vision recognizer, speech processor, and actuator) and then attempt to develop service robots by integrating these components. When prototypes are developed for a new application, or when services are added, modified, or removed from existing robots, unexpected, undesirable, and often dangerous side-effects, which are known as feature interaction problem, happen frequently. Reengineering of such robots can make a serious impact in delivery time and development cost.In this paper, we present our experience of re-engineering a prototype of a home service robot developed by Samsung Advanced Institute of Technology. First, we designed a modular and hierarchical software architecture that makes interaction among the components visible. With the visibility of interactions, we could assign functional responsibilities to each component clearly. Then, we re-engineered existing codes to conform to the new architecture using a reactive language Esterel. As a result, we could detect and solve feature interaction problems and alleviate the dificulty of adding or updating components.

An effective human-robot interaction is essential for wide penetration of mobile home robots into the market. The ability to track human in the home environment is one of fundamental functions of home robots. In this paper, we introduce a prototype of Samsung home robot (SHR) developed by Samsung Advanced Institute of Technology (SAIT) and present a scheme of tracking user movement utilizing a structured light range sensor. The structured light range sensor is considered to be cost-effective and robust for a wide range of environment; however, its low resolution of range data tends to extract wrong features for the human tracking, such as table legs, trash cans, door-edges, etc. To compromise this weakness, we have developed an algorithm that excludes those features that do not come from human legs. Efficacy and robustness of this proposed system were examined through experiments in the real home environment.