Towards modelling group-robot interactions using a qualitative spatial representation

This paper tackles the problem of finding a suitable qualitative representation for robots to reason about activity spaces where they carry out tasks interacting with a group of people. The Qualitative Spatial model for Group Robot Interaction (QS-GRI) defines Kendon-formations depending on: (i) the relative location of the robot with respect to other individuals involved in that interaction; (ii) the individuals' orientation; (iii) the shared peri-personal distance; and (iv) the role of the individuals (observer, main character or interactive). The evolution of Kendon-formations between is studied, that is, how one formation is transformed into another. These transformations can depend on the role that the robot have, and on the amount of people involved.Postprint (author's final draft

Real-time model-based video stabilization for microaerial vehicles

The emerging branch of micro aerial vehicles (MAVs) has attracted a great interest for their indoor navigation capabilities, but they require a high quality video for tele-operated or autonomous tasks. A common problem of on-board video quality is the effect of undesired movements, so different approaches solve it with both mechanical stabilizers or video stabilizer software. Very few video stabilizer algorithms in the literature can be applied in real-time but they do not discriminate at all between intentional movements of the tele-operator and undesired ones. In this paper, a novel technique is introduced for real-time video stabilization with low computational cost, without generating false movements or decreasing the performance of the stabilized video sequence. Our proposal uses a combination of geometric transformations and outliers rejection to obtain a robust inter-frame motion estimation, and a Kalman filter based on an ANN learned model of the MAV that includes the control action for motion intention estimation.Peer ReviewedPostprint (author's final draft

Cognitive robotics and control

Postprint (published version

Gaia X: federated open data in a trusted data space

Objectius de Desenvolupament Sostenible::4 - Educació de QualitatObjectius de Desenvolupament Sostenible::10 - Reducció de les Desigualtat

Cognitive human factors in the artificial intelligence of things

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksInternet of Things (IoT) systems are increasingly becoming complex. Heterogeneity in terms of hardware, software, computing capacity and connectivity is a source of complexity. The conversion of IoT systems into cyber-physical systems, including devices that are able not only to collect but also to process and take decisions, in real-time is a second source of complexity. Moreover, not only sensors should be considered, but also actuators, especially robots in the industry domain. In this context Artificial Intelligence (AI) technologies provide powerful capabilities to endow IoT devices with intelligent services, leading to the so-called Artificial Intelligence of Things (AIoT). In this context, the operator/user is in the middle of this complexity trying to understand the current situation and make effective real-time decisions. Hence, human factors, especially the cognitive ones, is a major issue to be addressed. New software development methods in the form of assistants and wizards are necessary to help operators/users to be context-aware and reduce their technical workload about coding or computer-oriented skills, focusing on the task/service at hands.Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (author's final draft

A real-time human-robot interaction system based on gestures for assistive scenarios

Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times.Postprint (author's final draft

A Qualitative Spatial Descriptor of Group-Robot Interactions

The problem of finding a suitable qualitative representation for robots to reason about activity spaces where they carry out tasks such as leading or interacting with a group of people is tackled in this paper. For that, a Qualitative Spatial model for Group Robot Interaction (QS-GRI) is proposed to define Kendon’s F-formations [16] depending on: (i) the relative location of the robot with respect to other individuals involved in that interaction; (ii) the individuals’ orientation; (iii) the shared peri-personal distance; and (iv) the role of the individuals (observer, main character or interactive). An iconic representation is provided and Kendon’s formations are defined logically. The conceptual neighborhood of the evolution of Kendon formations is studied, that is, how one formation is transformed into another. These transformations can depend on the role that the robot have, and on the amount of people involved.Postprint (published version

Progressive Design through Staged Evolution

Postprint (published version

Towards robots reasoning about group behavior of museum visitors: leader detection and group tracking

The final publication is available at IOS Press through http://dx.doi.org/10.3233/AIS-170467Peer ReviewedPostprint (author's final draft

Developing Cognitive Advisor Agents for Operators in Industry 4.0

Human cyber-physical systems (CPS) are an important component in the development of Industry 4.0. The paradigm shift of doing to thinking has allowed the emergence of cognition as a new perspective for intelligent systems. Currently, different platforms offer several cognitive solutions. Within this space, user assistance systems become increasingly necessary not as a tool but as a function that amplifies the capabilities of the operator in the work environment. There exist different perspectives of cognition. In this study cognition is introduced from the point of view of joint cognitive systems (JCSs); the synergistic combination of different technologies such as artificial intelligence (AI), the Internet of Things (IoT) and multi-agent systems (MAS) allows the operator and the process to provide the necessary conditions to do their work effectively and efficiently