Integrating IoT and assistive robots in the design of Ambient Assisted Living (AAL) frameworks has proven to be a useful solution for monitoring and assisting elderly people at home. As a way to manage the information captured and assess the person’s condition, respond to emergencies, promote physical or cognitive exercises, etc., these systems can also integrate a Virtual Caregiver (VC). Given the diversity of technologies deployed in such an AAL framework, deciding how to manage knowledge appropriately can be complex. This paper proposes to organise the AAL framework as a distributed system, i.e., as a collection of autonomous software agents that provide users with a single coherent response. In this distributed system, agents are deployed locally and handle replicas of the knowledge model. The problem of merging these replicas into a consistent representation, therefore arises.The δ -CRDT (Conflict-free Replicated Data Type) synchronisation mechanism is employed to ensure the eventual consistency with low communication overhead. To manage the dynamics of the AAL ecosystem, the δ -CRDT is combined with the publish/subscribe interaction protocol. In this way, the performance of the IoT, the robot and the VC, through the functionalities that depend on them, is efficiently adapted to changes in the context. To demonstrate the validity of the proposal, two use cases have been designed in which a collaborative response from the system is required. The first one deals with a possible fall of the user at home, while the second one deals with the problem of helping the person move small objects around the flat. The measured values of latency or consistency in the data show that the proposal works satisfactorily. • States an IoRT framework for AAL as a distributed system. • Guarantees the consistency of a distributed knowledge model using δ -CRDT. • Integrates δ -CRDT with the publish/subscribe interaction model. • Evaluation in real-world experiments shows that the approach is a practical solution.

The detection of a person’s eyes is a basic task in applications as important as iris recognition in biometric identification or fatigue detection in driving assistance systems. Current commercial and research systems use software frameworks that require a dedicated computer, whose power consumption, size and price are significantly large. This paper presents a hardware-based embedded solution for eye detection in real-time. From an algorithmic point-of-view, the popular Viola–Jones approach has been redesigned to enable highly parallel, single-pass image-processing implementation. Synthesized and implemented in an All-Programmable System-on-Chip (AP SoC), this proposal allows us to process more than 88 frames per second (fps), taking the classifier less than 2 ms per image. Experimental validation has been successfully addressed in an iris recognition system that works with walking subjects. In this case, the prototype module includes a CMOS digital imaging sensor providing 16 Mpixels images, and it outputs a stream of detected eyes as 640 × 480 images. Experiments for determining the accuracy of the proposed system in terms of eye detection are performed in the CASIA-Iris-distance V4 database. Significantly, they show that the accuracy in terms of eye detection is 100%.

The introduction of robots in the real world requires previous evaluation of the envisaged performance. Factors like usability, user experience, social acceptance, or societal impact, among others, have been taken into account in evaluation frameworks defined during the last years. However, one of the most important factors that need to be evaluated in any kind of interaction is whether all the users are able to work with the system with the same opportunities and easiness, and none of the current human–robot interaction (HRI) evaluation frameworks include this factor yet. This article proposes an extension of a popular HRI evaluation framework, including accessibility as a new evaluation factor. The proposed approach, named AUSUS, considers Accessibility, Usability, Social acceptance, User experience, and Societal impact. This article presents a use case of the framework, which is evaluated through a socially assistive robotic platform created to perform comprehensive geriatric assessment: the CLARC system. The details of the evaluation process in a hospital and a retirement home are reported, and the main difficulties and recommendations of using AUSUS are discussed.

Socially assistive robots appear as a powerful tool in the upcoming silver society. They are among the technologies for Assisted Living, offering a natural interface with smart environments, while helping people through social interaction. The CLARC project aims to develop a socially assistive robot to help clinicians perform Comprehensive Geriatric Assessment (CGA) procedures. This robot autonomously drives some tests and processes, saving time for the clinician to perform more added-value activities, like designing care plans. The project has recently finished its first two phases, and now it faces its final one. This paper details the current prototype of the CLARC system and the main results collected so far during its evaluation. Then, it describes the updates and modifications planned for the next year, in which long term extensive evaluations will be conducted to validate its acceptability and utility.

SUMMARY Social robots must adapt to dynamic environments, human interaction partners and challenging new stringent tasks. Their inner software is usually distributed and should be designed and deployed carefully because slight changes in the robot's requirements can have an important impact not only on the existing source code but also on the resulting performance at run‐time. This paper proposes an implementation of this inner software using a new lightweight middleware for networked robotics calledNerve. The principal novelty this middleware has with respect to other state‐of‐the‐art approaches is that it guarantees both scalability and QoS, which are key requirements for real‐time robotics software. The benefits ofNervehave been proved through its use in two key components of the cognitive system of a social robot: (i) the visual attention mechanism, used to extract relevant data from perceived images; and (ii) a robot learning by imitation control architecture that allows the social robot to be taught by people using natural demonstrations (i.e. using the same communication channels that would be used to teach people).Nervemakes use of existing patterns for networked applications together with the recent Data Distribution Service specification, which is a publish/subscribe standard for real‐time and distributed systems that provides a wide set of QoS policies. In this paper, these different QoS policies have been applied carefully to achieve the best performance of the target robot. Copyright © 2012 John Wiley & Sons, Ltd.

This paper proposes an incremental scheme for visual landmark learning and recognition. The feature selection stage characterises the landmark using the Opponent SIFT, a color-based variant of the SIFT descriptor. To reduce the dimensionality of this descriptor, an incremental non-parametric discriminant analysis is conducted to seek directions for efficient discrimination (incremental eigenspace learning). On the other hand, the classification stage uses the incremental envolving clustering method (ECM) to group feature vectors into a set of clusters (incremental prototype learning). Then, the final classification is conducted based on the k-nearest neighbor approach, whose prototypes were updated by the ECM. This global scheme enables a classifier to learn incrementally, on-line, and in one pass. Besides, the ECM allows to reduce the memory and computation expenses. Experimental results show that the proposed recognition system is well suited to be used by an autonomous mobile robot.

The ability to detect perceptions which were never experienced before, i.e. novelty detection, is an important component of autonomous robots working in real environments. It is achieved by comparing current data provided by its sensors with a previously known map of the environment. This often constitutes an extremely challenging task due to the large amounts of data that must be compared in real-time. With respect to previously proposed approaches, this paper detects changes in 3D environment based on probabilistic models, the Gaussian Mixture Model, and a fast and robust combined constraint matching algorithm. The matching allows to represent the scene view as a graph which emerges from the comparison between Mixtures of Gaussians. Finding the largest set of mutually consistent matches is equivalent to find the maximum clique on a graph. The proposed approach has been tested for mobile robotics purposes in real environments and compared to other matching algorithms. Experimental results demonstrate the performance of the proposal.

Hybrid maps combine metric and topological information for efficiently managing large-scale environments. In a feature-based mapping framework, this paper describes the application of a spectral clustering approach for automatically detecting the transitions between subsequently traversed local maps. Contrary to recently proposed approaches, this algorithm considers each individual map feature as a node of a graph whose edges link two nodes if they are simultaneously observed. Thus, given a sequence of observations, an auxiliary graph is incrementally built whose edges carry non-negative weights according to the locality of the features. Given a feature, its locality defines the set of features that has been observed simultaneously with it at least once. At each execution of the mapping approach, the feature-based graph is split into two subgraphs using a normalized spectral clustering algorithm. If the graph partition is validated, the algorithm determines that the robot is moving into a new area and a new local map is generated. We have tested the proposed approach in real environments where features are obtained using 2D laser sensors or vision. Experimental results demonstrate the performance of the proposal.

In this paper, we describe a new approach which uses scale-invariant image features to estimate the motion of a stereo head. These point features are matched between pairs of frames and linked into image trajectories at video rate, generating what it is called visual odometry, i.e. motion estimates from visual input alone. With respect to previously proposed approaches, the main novelty of our proposal is that the matching between sets of features associated to stereo pairs and between sets of image features associated to consecutive frames are conducted by means of a fast combined constraint matching algorithm. Besides, the efficiency of the approach is increased by using a closed-form solution to estimate the final robot displacement between consecutive acquired frames. We have tested the proposed approach for navigational purposes in a real environment. Experimental results demonstrate the performance of the proposal.

In this paper, an approach for fitting a circle to 2-D data which represent only a small part of the curve is described. This approach deals with the particular case where data is specified in terms of its Cartesian coordinates and the errors in both coordinates are not independent. Besides, an associated uncertainty ellipse which describes the measurement error and the variance matrix associated to the estimated parameters are obtained. This method is particularly well designed to fit a circle to a set of measured range readings provided by a 2-D laser range finder when these range readings are specified in terms of its Cartesian coordinates. Therefore, it has been successfully applied to acquire circle-shaped landmarks in a mobile robotics navigation task.

This paper presents a geometrical feature detection system to use with conventional 2D laser rangefinders. This system consists of three main modules: data acquisition and pre-processing, rupture and breakpoint detection and feature extraction. The novelty of this system is a new efficient approach for natural feature extraction based on curvature estimation. This approach permits to extract and characterise line segments, corners and curve segments from the laser scan. Experimental results show that the proposed approach is very fast and permit to verify its effectiveness in indoor and outdoor environments

This paper presents a visual attention mechanism for the acquisition of landmarks in an arbitrary scene. The proposed mechanism consists of two consecutive selection stages. The first one employs classical preattentive saliency computa tions to select a reduced set of interest regions from the whole input image (data-driven stage). The second stage selects from the output of the first selection stage the region that can be considered as a potential landmark (model-driven stage). This potential landmark is the input of the attentive stage, that must characterize it and finally determine if this object is a real landmark. The used imaging sensor is a stereo vision system which is capable of providing depth data as well as color images. This stereo vision system is mounted on an autonomous mobile robot and serves map-building and localisation purposes. We present results achieved by applying the proposed visual attention scheme to on-line acquired stereo pairs of indoor and outdoor scenes.

In object tracking, change of objects aspect is the most important cause of failure. This paper proposes a modified template matching approach to solve this problem without a priori learning of object views. This method permits to track non-rigid objects in real-time by employing a weighted template, which is dynamically updated, and a hierarchical framework that integrates all the components of the tracker. The capability of the tracking system to handle partial occlusions and target distortions is demonstrated for several video sequences.

Research in mobile robot navigation has been based in approaches that integrate the metric and topological paradigms for mapping indoor environments. While metric methods produce accurate environment representations, their huge data volume and time complexity often prohibits efficient planning in large-scale indoor environments. On the other hand, topological maps can be used in a more efficient way, but accurate and coherent topological maps are often difficult to learn and maintain in large-scale environments. The paper describes an approach that integrates both paradigms: metric and topological. The metric map is learnt using a very simple scheme based on the works of Moravec and Elfes (1988). Then, a topological map is generated on top of the metric map by using a hierarchical structure. The main advantage of the proposed structure is that the partitioning of the metric map into coherent regions is achieved in an unsupervised manner with a low computational time. The paper gives results for autonomous exploration, mapping and planning of a Nomad200 mobile robot in large indoor environments.

This paper presents both a new sonar based place learning method and a global localisation algorithm based on finite Markov chains for autonomous robots. Landmarks are calculated by projecting the Fourier transform of the depth map obtained from a ring of equally spaced sonar sensors onto a bidimensional base of its vectorial subspace. Resulting landmarks can be acquired at any position of the environment and they do not depend on the robot orientation. Localisation relies on segmenting available landmarks into homogeneous regions and calculating transition matrices between them. Then, the position of the robot is estimated according to finite Markovian chains, but the probability of occupying a given region is modulated by the most recently acquired landmark. The method is valid for complex unstructured environments and it has experimentally proven to be fast, reliable and computationally cheap.

Presents a fast localisation algorithm for autonomous mobile agents in dynamic environments based on the definition of a very small sized landmark type. These landmarks are calculated by obtaining the coordinates of the circular depth function obtained from a ring of equally spaced sonar sensors projected on a bi-dimensional base of a vectorial space. Finally, a pyramidal structure is used to enhance and fasten the performance of the localisation algorithm.