The increasing number of robots in home environments leads to an emerging coexistence between humans and robots. Robots undertake common tasks and support the residents in their everyday life. People appreciate the presence of robots in their environment as long as they keep the control over them. One important aspect is the control of a robot's workspace. Therefore, we introduce virtual borders to precisely and flexibly define the workspace of mobile robots. First, we propose a novel framework that allows a person to interactively restrict a mobile robot's workspace. To show the validity of this framework, a concrete implementation based on visual markers is implemented. Afterwards, the mobile robot is capable of performing its tasks while respecting the new virtual borders. The approach is accurate, flexible and less time consuming than explicit robot programming. Hence, even non-experts are able to teach virtual borders to their robots which is especially interesting in domains like vacuuming or service robots in home environments.
@cite_30: Numerous robots have been developed, and some of them are already being used in homes, institutions, and workplaces. Despite the development of useful robot functions, the focus so far has not been on user interfaces of robots. General users of robots find it hard to understand what the robots are doing and what kind of work they can do. This paper presents an interface for the commanding home robots by using stroke gestures on a computer screen. This interface allows the user to control robots and design their behaviors by sketching the robot's behaviors and actions on a top-down view from ceiling cameras. To convey a feeling of directly controlling the robots, our interface employs the live camera view. In this study, we focused on a house-cleaning task that is typical of home robots, and developed a sketch interface for designing behaviors of vacuuming robots.
@cite_28: Semantic information can help both humans and robots to understand their environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a semantic mapping approach through human activity recognition in an indoor human-robot coexisting environment. An intelligent mobile robot platform can create a 2D metric map, while human activity can be recognized using motion data from wearable motion sensors mounted on a human subject. Combined with pre-learned models of activity-to-furniture type association and robot pose estimates, the robot can determine the distribution of the furniture types on the 2D metric map. Simulations and real world experiments demonstrate that the proposed method is able to create a reliable metric map with accurate semantic information.
@cite_1: A virtual wall system (10) is composed of a mobile robotic device (11) and at least one virtual wall generator (21). The mobile robotic device (11) includes a steering unit (12) for steering itself toward at least one direction, a steering control unit (14) connected with the steering unit (12) for controlling the steering of the steering unit (12), and at least one sonic receiver (18) mounted to one side thereof for receiving sonic signals. The at least one virtual wall generator (21) is placed on a planar surface where the mobile robotic device (11) moves, having a sonic emitter (26) and a taper-shaped hole (22) facing sidewards. The taper-shaped hole (22) increasingly expands from inside out. The sonic emitter (26) is to emit sonic signals that are directive subject to the taper-shaped hole (22). The at least one virtual wall generator (21) further includes a power module (29) as power supply.
@cite_24: 
@cite_11: We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following different findings in spatial cognition, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system.
In contrast to mapping physical environments, virtual borders have to be mapped in a different way. @cite_30 propose a stroke-based interface on a tablet PC to control the workspace of a vacuum cleaning robot. This technique needs several top-down view cameras in the robot's environment to cover the whole working area. Commercial solutions to the problem of defining the robot's workspace comprise magnetic strips that are placed on the ground to indicate the borders @cite_24 or beacon devices to generate infrared signals @cite_1 . These conic beams will not be crossed by the robot and can be used to block doors or corridors. The disadvantages are intrusiveness, low flexibility and energy consumption. Other explicit methods to incorporate additional information into maps encompass situated dialogues @cite_11 or activity recognition based on wearables @cite_28 . These works integrate semantics into maps, but they do not allow the flexible definition of certain areas, e.g. arbitrary polygons inside a room. The flexibility of our approach distinguishes our work from previous works.