Human-aware navigation for autonomous mobile robots for intra-factory logistics

This paper presents a human-aware navigation system for mobile robots targeted to cooperative assembly in intra-factory logistics scenarios. To improve overall efficiency of the operator-robot ensemble, assembly stations and operators are modelled as cost functions in a layered cost map supporting the robot navigation system. At each new sensory update, the system uses each operator’s estimated location to affect the cost map accordingly. To promote predictability and comfort in the human operator, the cost map is affected according to the Proxemics theory, properly adapted to take into account the layout activity space of the station in which the operator is working. Knowledge regarding which task and station are being handled by the operator are assumed to be given to the robot by the factory’s computational infrastructure. To foster integration in existing robots, the system is implemented on top of the navigation system of the Robot Operating System (ROS).info:eu-repo/semantics/acceptedVersio

Me and My Robot Smiled at One Another: The Process of Socially Enacted Communicative Affordance in Human-Machine Communication

The term affordance has been inconsistently applied both in robotics and communication. While the robotics perspective is mostly object-based, the communication science view is commonly user-based. In an attempt to bring the two perspectives together, this theoretical paper argues that social robots present new social communicative affordances emerging from a two-way relational process. I first explicate conceptual approaches of affordance in robotics and communication. Second, a model of enacted communicative affordance in the context of Human-Machine Communication (HMC) is presented. Third and last, I explain how a pivotal social robot characteristic—embodiment—plays a key role in the process of social communicative affordances in HMC, which may entail behavioral, emotional, and cognitive effects. The paper ends by presenting considerations for future affordance research in HMC

Human-Robot Motion: Taking Attention into Account

Mobile robot companions are service robots that are mobile and designed to share our living space. For such robots, mobility is essential and their coexistence with humans adds new aspects to the mobility issue: the first one is to obtain appropriate motion and the second one is interaction through motion. We encapsulate these two aspects in the term Human-Robot Motion (HRM) with reference to Human-Robot Interaction. The long-term issue is to design robot companions whose motions, while remaining safe, are deemed appropriate from a human point of view. This is the key to the acceptance of such systems in our daily lives. The purpose of this paper is to explore how the psychological concept of attention can be taken into account in HRM. To that end, we build upon an existing model of attention that computes an attention matrix that describes how the attention of each person is distributed among the different elements, persons and objects, of the environment. Using the attention matrix, we propose the novel concept of attention field that can be viewed as an attention predictor. Using different case studies, we show how the attention matrix and the attention field can be used in HRM.Les robots compagnons mobiles sont des robots de service conçus pour partager et se déplacer dans notre espace de vie. Pour de tels robots, la mobilité est essentielle et leur coexistence avec des humains ajoute de nouveaux aspects à ce sujet de recherche: le premier est d'obtenir un mouvement approprié et le second est l'interaction au travers du mouvement. On regroupe ces deux aspects sous le terme Human-Robot Motion (HRM) en référence à Human-Robot Interaction. L'objectif à long terme est la conception de robots compagnons dont le mouvement, tout en restant sans danger, est jugé approprié d'un point de vue humain. Ceci est la clé de l'acceptation de tels systèmes dans notre vie quotidienne. L'objectif de ce papier est d'explorer comment le concept psychologique d'attention peut être prix en compte dans HRM. A cette fin, nous proposons un concept nouveau de champ attentionnel qui peut être vu comme un prédicteur attentionnel. Nos travaux se basent sur un modèle existant qui quantifie l'attention humaine et fournit une matrice attentionnelle qui décrit la distribution des ressources attentionnelles de chaque personne entre les différents éléments, personnes et objets de son environnement. Le calcul du champ attentionnel introduit découle de cette matrice attentionnelle. En considérant différents scénarios d'étude, on montre comment la matrice et le champ attentionnel(le) peuvent être utilisés en HRM

Human Robot Motion: A Shared Effort Approach

International audienceThis paper is about Human Robot Motion (HRM), i.e. the study of how a robot should move among humans. This problem has often been solved by considering persons as moving obstacles, predicting their future trajectories and avoiding these trajectories. In contrast with such an approach, recent works have showed benefits of robots that can move and avoid collisions in a manner similar to persons, what we call human-like motion. One such benefit is that human-like motion was shown to reduce the planning effort for all persons in the environment, given that they tend to solve collision avoidance problems in similar ways. The effort required for avoiding a collision, however, is not shared equally between agents as it varies depending on factors such as visibility and crossing order. Thus, this work tackles HRM using the notion of motion effort and how it should be shared between the robot and the person in order to avoid collisions. To that end our approach learns a robot behavior using Reinforcement Learning that enables it to mutually solve the collision avoidance problem during our simulated trials

Modeling a Social Placement Cost to Extend Navigation Among Movable Obstacles (NAMO) Algorithms

DOI is not yet properly functionnal, go to IEEEXplore directly : https://ieeexplore.ieee.org/abstract/document/9340892International audienceCurrent Navigation Among Movable Obstacles (NAMO) algorithms focus on finding a path for the robot that only optimizes the displacement cost of navigating and moving obstacles out of its way. However, in a human environment, this focus may lead the robot to leave the space in a socially inappropriate state that may hamper human activity (i.e. by blocking access to doors, corridors, rooms or objects of interest). In this paper, we tackle this problem of "Social Placement Choice" by building a social occupation costmap, built using only geometrical information. We present how existing NAMO algorithms can be extended by exploiting this new cost map. Then, we show the effectiveness of this approach with simulations, and provide additional evaluation criteria to assess the social acceptability of plans

Human-Robot Motion: Taking Attention into Account

Mobile robot companions are service robots that are mobile and designed to share our living space. For such robots, mobility is essential and their coexistence with humans adds new aspects to the mobility issue: the first one is to obtain appropriate motion and the second one is interaction through motion. We encapsulate these two aspects in the term Human-Robot Motion (HRM) with reference to Human-Robot Interaction. The long-term issue is to design robot companions whose motions, while remaining safe, are deemed appropriate from a human point of view. This is the key to the acceptance of such systems in our daily lives. The purpose of this paper is to explore how the psychological concept of attention can be taken into account in HRM. To that end, we build upon an existing model of attention that computes an attention matrix that describes how the attention of each person is distributed among the different elements, persons and objects, of the environment. Using the attention matrix, we propose the novel concept of attention field that can be viewed as an attention predictor. Using different case studies, we show how the attention matrix and the attention field can be used in HRM.Les robots compagnons mobiles sont des robots de service conçus pour partager et se déplacer dans notre espace de vie. Pour de tels robots, la mobilité est essentielle et leur coexistence avec des humains ajoute de nouveaux aspects à ce sujet de recherche: le premier est d'obtenir un mouvement approprié et le second est l'interaction au travers du mouvement. On regroupe ces deux aspects sous le terme Human-Robot Motion (HRM) en référence à Human-Robot Interaction. L'objectif à long terme est la conception de robots compagnons dont le mouvement, tout en restant sans danger, est jugé approprié d'un point de vue humain. Ceci est la clé de l'acceptation de tels systèmes dans notre vie quotidienne. L'objectif de ce papier est d'explorer comment le concept psychologique d'attention peut être prix en compte dans HRM. A cette fin, nous proposons un concept nouveau de champ attentionnel qui peut être vu comme un prédicteur attentionnel. Nos travaux se basent sur un modèle existant qui quantifie l'attention humaine et fournit une matrice attentionnelle qui décrit la distribution des ressources attentionnelles de chaque personne entre les différents éléments, personnes et objets de son environnement. Le calcul du champ attentionnel introduit découle de cette matrice attentionnelle. En considérant différents scénarios d'étude, on montre comment la matrice et le champ attentionnel(le) peuvent être utilisés en HRM

How Should a Robot Approach a Pair of People?

This thesis experimentally investigates the comfort of pairs of seated people when they are approached by a robot from different directions. While the effect of robot approach direction on the comfort of a lone person has been investigated previously, the extension to a robot approaching pairs of people has not been explored rigorously. Three maximally-different seating configurations of paired people and eight different robot approach directions were considered. The experiment was augmented with a fourth seating configuration of a lone individual, allowing the responses of grouped and lone participants to be compared. Data obtained from the experiment were analysed using both linear and directional statistics. Results from 180 unique participants showed that the comfort of a person when a robot approached is influenced by the presence and location of a second person. Analysis of these data with directional statistics showed that participant comfort preference clusters into angular regions of ‘suitable for robot approach’ and ‘unsuitable for robot approach’. This finding shows the importance of avoiding robot approach directions of low comfort, rather than selecting a singular robot approach direction of high comfort. Rayleigh’s test of uniformity, a directional statistics method, also shows across all participant configurations that robot approach directions that minimize participant discomfort align spatially with regions that allow for good line of sight of the robot by both people, and are centred on the largest open space that a robot could approach the group from. Participants who were grouped also regarded the robot as having more social agency than did lone experimental participants. Grouped participants were less frustrated with the experimental task and also found it less physically and temporally demanding in comparison to lone experimental participants

Human-Machine Communication: Complete Volume. Volume 1

This is the complete volume of HMC Volume 1