In general, the problems of objects' and rooms' categorizations for robotic applications have been addressed separately. The current trend is, however, towards a joint modelling of both issues in order to leverage their mutual contextual relations: object → room (e.g. the detection of a microwave indicates that the room is likely to be a kitchen), and room → object (e.g. if the robot is in a bathroom, it is probable to find a toilet). Probabilistic Graphical Models (PGMs) are typically employed to conveniently cope with such relations, relying on inference processes to hypothesize about objects' and rooms' categories. In this work we present a Conditional Random Field (CRF) model, a particular type of PGM, to jointly categorize objects and rooms from RGBD images exploiting object-object and object-room relations. The learning phase of the proposed CRF uses Human Knowledge (HK) to eliminate the necessity of gathering real training data. Concretely, HK is acquired through elicitation and codified into an ontology, which is exploited to effortless generate an arbitrary number of representative synthetic samples for training. The performance of the proposed CRF model has been assessed using the NYU2 dataset, achieving a success of ~ 70% categorizing both, objects and rooms.

The eight papers in this special section focus on computer-based intelligent technologies for improving the quality of life.

Robotic telepresence is a promising tool for enhancing remote communications in a variety of applications. It enables a person to embody a robot and interact within a remote place in a direct and natural way. A particular scenario where robotic telepresence demonstrates its advantages is in elder telecare applications in which a caregiver regularly connects to the robots deployed at the apartments of the patients to check their health. Normally, in these cases, the caregiver may encounter additional problems in guiding the robot because s/he is not familiar with the houses. In this paper we describe a procedure to remotely create and to exploit different types of maps for facilitating the guidance of a telepresenc e robot. Our work has been implemented and successfully tested on the Giraff telepresence robot.

Most successful mobile robots rely on 2D radial laser scanners for perceiving the environment. The use of these sensors for reactive navigation has a serious limitation: the robot can only detect obstacles in the plane scanned by the sensor, with the consequent risk of collision with objects out of this plane. The recent commercialization of RGB-D cameras, like Kinect, opens new possibilities in this respect. In this paper we address the matter of adding the 3D information provided by these cameras to a reactive navigator designed to work with radial laser scanners. We experimentally analyze the suitability of Kinect to detect small objects and propose a simple but effective method to combine readings from both type of sensors as well as to overcome some of the drawbacks that Kinect presents. Experiments with a real robot and a particular reactive algorithm have been conducted, proving a significant upgrade in performance.

Telepresence robots are teleoperated robotic systems that allow users to virtually visit a remote place and interact with the environment through their sensorial and motor capabilities. This technology has a great potential to facilitate remote interaction and social communication with people, in particular with the elderly. This paper presents some technical improvements for one of such a robot: the Giraff telepresence platform. These improvements, raised by users' experience, are related to a safer and easier driving of the platform, including auto-docking to the recharging station, obstacle detection, and displaying the robot position in a sketch map of the visited place.

Small computers used for assisting drivers (mostly in finding routes) have been growing in popularity in the past years. These systems are inherently interactive, but up to now this interaction is tackled under rather simple approaches. For example, current routing computer assistants consider only the shortest or the quickest route to a destination, although in certain situations it could be interesting for the driver to take into consideration other factors, such as the criminal rate, land value, or the beauty of the areas to be traversed. On the other hand, the interactive processes between the driver and the routing assistant are still very limited: They only enable the user to discard (or suggest) particular locations through a fixed set of names, i.e. street's names. This paper proposes a novel interactive mechanism for in-vehicle routing that uses topological information at different levels of detail and a multihierarchical representation of urban maps. These hierarchical representations permit the system not only to plan routes efficiently but also to report them at different levels in detail in a human-like set of symbols adapted to each user. This enhances the human–computer interaction during the routing process, increasing driver satisfaction. We illustrate our technique through a case of study in the city of Málaga (Spain). © 2010 Wiley Periodicals, Inc.

Nowadays, the availability of high-resolution images taken from satellites, like Quickbird, Orbview, and others, offers the remote sensing community the possibility of monitoring and surveying vast areas of the Earth for different purposes, e.g. monitoring forest regions for ecological reasons. A particular application is the use of satellite images to survey the bottom of the seas around the Iberian peninsula which is flooded with innumerable treasures that are being plundered by specialized ships. In this paper we present a GIS-based application aimed to catalog areas of the sea with archeological interest and to monitor the risk of plundering of ships that stay within such areas during a suspicious period of time.

Detecting abnd localizing objects from space, like roads, rivers, lakes, etc., is a challenging task with multiple applications in remote sensing. In this paper we address the detection of swimming pools in colour high-resolution images of urban areas acquired by the Quickbird satellite. The main motivation of this work is to survey and localize filled swimming pools during drought periods, fact that should be then punished by the local authorities. The proposed algorithm applies colour analysis for water detection and approximate segmentation, as an initial, rough localization, and active contours techniques to refine the pools' shape. We have tested our algorithm in both satellite and aerial images with satisfactory results.

To date, no solution has been proposed to human-machine interactive task planning that deals simultaneously with two important issues: 1) the capability of processing large amounts of information in planning (as it is needed in any real application) and 2) being efficient in human-machine communication (a proper set of symbols for human-machine interaction may not be suitable for efficient automatic planning and vice versa). In this paper, we formalize a symbolic model of the environment to solve these issues in a natural form through a human-inspired mechanism that structures knowledge in multiple hierarchies. Planning with a hierarchical model may be efficient even in cases where the lack of hierarchical information would make it intractable. However, in addition, our multihierarchical model is able to use the symbols that are most familiar to each human user for interaction, thus achieving efficiency in human-machine communication without compromising the task-planning performance. We formalize here a general interactive task-planning process which is then particularized to be applied to a mobile robotic application. The suitability of our approach has been demonstrated with examples and experiments.

The use of the World Wide Web for robot teleoperation is growing in the last years due mainly to the pervasiveness of Internet and Web browsers, although Web interfaces usually use Ethernet networks that exhibit time unpredictability. Most recent research in the area has been focused on improving time predictability of the network under delays, jitter, and no guaranteed bandwidth. However, we believe that: i) not only the network, but every component in the interfaced system exhibit time unpredictability; and ii) improving time predictability is not the only solution: adapting the interfaced system to unpredictable conditions is also a possibility. In this paper we consider a Web interfaced robot as a set of control loops and describe and implement a hysteresis controller for regulating the flow of information through the loops as a method to satisfy the system time requirements under some unpredictable and varying conditions. For demonstrating the goodness of our algorithm, we: a) compare it with a near-optimal one automatically generated through reinforcement learning, and b) show an implementation of the algorithm for the direct teleoperation of a service mobile robot, obtaining a better behavior than the same system without flow regulation.

The use of a symbolic model of the spatial environment becomes crucial for a mobile robot that is intended to operate optimally and intelligently in indoor scenarios. Constructing such a model involves important problems that are not solved completely at present. One is called anchoring, which implies to maintain a correct dynamic correspondence between the real world and the symbols in the model. The other problem is adaptation: among the numerous possible models that could be constructed for representing a given environment, optimization involves the selection of one that improves as much as possible the operations of the robot. To cope with both problems, in this paper, we propose a framework that allows an indoor mobile robot to learn automatically a symbolic model of its environment and to optimize it over time with respect to changes in both the environment and the robot operational needs through an evolutionary algorithm. For coping efficiently with the large amounts of information that the real world provides, we use abstraction, which also helps in improving task planning. Our experiments demonstrate that the proposed framework is suitable for providing an indoor mobile robot with a good symbolic model and adaptation capabilities.

Completely autonomous performance of a mobile robot within noncontrolled and dynamic environments is not possible yet due to different reasons including environment uncertainty, sensor/software robustness, limited robotic abilities, etc. But in assistant applications in which a human is always present, she/he can make up for the lack of robot autonomy by helping it when needed. In this paper, the authors propose human-robot integration as a mechanism to augment/improve the robot autonomy in daily scenarios. Through the human-robot-integration concept, the authors take a further step in the typical human-robot relation, since they consider her/him as a constituent part of the human-robot system, which takes full advantage of the sum of their abilities. In order to materialize this human integration into the system, they present a control architecture, called architecture for human-robot integration, which enables her/him from a high decisional level, i.e., deliberating a plan, to a physical low level, i.e., opening a door. The presented control architecture has been implemented to test the human-robot integration on a real robotic application. In particular, several real experiences have been conducted on a robotic wheelchair aimed to provide mobility to elderly people.

Most research in networked robots that use Web interfaces for robot control has been focused recently on the network part, since Ethernet involves poor (unpredictable) time performance. However, we believe that the problem to be addressed is more general and should not be restricted only to communication engineering: the interfaced system as a whole should adapt to get the most from the user, from the connection, and from the robot, even when no strict performance is possible. For that purpose, this paper introduces a new architecture for Web remote operation of robots that exhibits a high degree of flexibility in its adaptation to each particular user (through modular, configurable JAVA applets), to the system time-varying performance (through probability-guided, run-time adaptation of control loops), and to the robot software architecture (the standard CORBA is assumed as its middleware). Our approach constitutes an initial step for adapting comprehensively to all the mentioned issues, hence permitting to be employed in very different scenarios: realtime control, telecare, remote surveillance, etc.

The success of mobile robots, and particularly of those interfacing with humans in daily environments (e.g., assistant robots), relies on the ability to manipulate information beyond simple spatial relations. We are interested in semantic information, which gives meaning to spatial information like images or geometric maps. We present a multi-hierarchical approach to enable a mobile robot to acquire semantic information from its sensors, and to use it for navigation tasks. In our approach, the link between spatial and semantic information is established via anchoring. We show experiments on a real mobile robot that demonstrate its ability to use and infer new semantic information from its environment, improving its operation.

Task planning in mobile robotics should be performed efficiently, due to real-time requirements of robot-environment interaction. Its computational efficiency depends both on the number of operators (actions the robot can perform without planning) and the size of the world states (descriptions of the world before and after the application of operators). Thus, in real robotic applications, where both components can be large, planning may turn inefficient, and even unsolvable. In the artificial intelligence (AI) literature on planning, little attention has been put into efficient management of large-scale world descriptions. In real large-scale situations, conventional AI planners (in spite of the most modern improvements) may consume intractable amounts of storage and computing time, due to the huge amount of information. This paper proposes a new approach to task planning called "hierarchical task planning through world abstraction" that, by hierarchically arranging the world representation, becomes a good complement of Stanford Research Institute Problem Solver-style planners, significantly improving their computational efficiency. Broadly speaking, our approach works by first solving the task-planning problem in a highly abstracted model of the environment of the robot, and then refines the solution under more detailed models, where irrelevant world elements can be ignored, due to the results previously obtained at abstracted levels. Among the different implementations that can be made with our general strategy, we describe two that use a graph-based hierarchical world representation named the "annotated and hierarchical" graph. We show experiments, as well as results of a mobile robot operating in a large-scale environment, that demonstrate an important improvement in the efficiency of our algorithms with respect to conventional (both hierarchical and nonhierarchical) planning and their nice integration with existing planners.