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A critical demand for innovation in elderly care, especially in delivering the quality of service to elderlies, arises as many parts of the world are rapidly in transition to an elderly society. The advancement of robotic technologies, especially, in cognitive robotics and sociable human-robot interaction offers a great opportunity for meeting such demand. This paper presents the design and implementation of a next generation of elderly care robot, named as "HomeMate," based on an innovative undertaking in its sociability and dependability with extensive user studies. The elderlies taken care of by Senior Welfare Centers are chosen as the target sector, for which the following five service scenarios are designed: infotainment, video chatting, game playing, medicine alarm and, in particular, errand services. Unlike conventional approaches, the scenarios are designed here to ensure the overall quality of service by maximizing the synergy under an elderly-caregiver-service robot ecosystem. The unique features implemented in HomeMate include 1) the principle of affordance in appearance design by matching functionality and anthropomorphism indices, 2) the sociability implemented by balancing between autonomy and user controllability as well as by integrating multimodal interactions into HomeMate avatar, and 3) the emphasis on dependability to inspire confidence on HomeMate as a trusted assistant, for which the principle of dependability is proposed and implemented with a cognitive framework. Experiments and user studies strongly support the proposed design principles and verify the dependability in performance. [ABSTRACT FROM AUTHOR]

This paper describes a scheme for proactive human search for a designated person in an undiscovered indoor environment without human operation or intervention. In designing and developing human identification with prior information a new approach that is robust to illumination and distance variations in the indoor environment is proposed. In addition, a substantial exploration method with an octree structure, suitable for path planning in an office configuration, is employed. All these functionalities are integrated in a message- and component-based architecture for the efficient integration and control of the system. This approach is demonstrated by succeeding human search in the challenging robot mission of the 2009 Robot Grand Challenge Contest. [ABSTRACT FROM AUTHOR]

Emerged as salient in the recent home appliance consumer market is a new generation of home cleaning robot featuring the capability of Simultaneous Localization and Mapping (SLAM). SLAM allows a cleaning robot not only to self-optimize its work paths for efficiency but also to self-recover from kidnappings for user convenience. By kidnapping, we mean that a robot is displaced, in the middle of cleaning, without its SLAM aware of where it moves to. This paper presents a vision-based kidnap recovery with SLAM for home cleaning robots, the first of its kind, using a wheel drop switch and an upward-looking camera for low-cost applications. In particular, a camera with a wide-angle lens is adopted for a kidnapped robot to be able to recover its pose on a global map with only a single image. First, the kidnapping situation is effectively detected based on a wheel drop switch. Then, for an efficient kidnap recovery, a coarse-to-fine approach to matching the image features detected with those associated with a large number of robot poses or nodes, built as a map in graph representation, is adopted. The pose ambiguity, e.g., due to symmetry is taken care of, if any. The final robot pose is obtained with high accuracy from the fine level of the coarse-to-fine hierarchy by fusing poses estimated from a chosen set of matching nodes. The proposed method was implemented as an embedded system with an ARM11 processor on a real commercial home cleaning robot and tested extensively. Experimental results show that the proposed method works well even in the situation in which the cleaning robot is suddenly kidnapped during the map building process. [ABSTRACT FROM AUTHOR]

Robot intelligence requires a real-time connection between sensing and action. A new computation principle of robotics that efficiently implements such a connection is utmost important for the new generation of robotics. In this paper, a perception–action network is presented as a means of efficiently integrating sensing, knowledge, and action for sensor fusion and planning. The network consists of a number of heterogeneous computational units, representing feature transformation and decision-making for action, which are interconnected as a dynamic system. New input stimuli to the network invoke the evolution of network states to a new equilibrium, through which a real-time integration of sensing, knowledge, and action can be accomplished. The network provides a formal, yet general and efficient, method of achieving sensor fusion and planning. This is because the uncertainties of signals, propagated in the network, can be controlled by modifying sensing parameters and robot actions. Algorithms for sensor planning based on the proposed network are established and applied to robot self-localization. Simulation and experimental results are shown. [ABSTRACT FROM AUTHOR]

A correction to the article "Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots" that was published in the 2014 issue is presented.