ロボットナビゲーションにおける人間への意図伝達に関する研究

早大学位記番号:新7327早稲田大

How to Communicate Robot Motion Intent: A Scoping Review

Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.Comment: Interactive Data Visualization of the Paper Corpus: https://rmi.robot-research.d

Explainable shared control in assistive robotics

Shared control plays a pivotal role in designing assistive robots to complement human capabilities during everyday tasks. However, traditional shared control relies on users forming an accurate mental model of expected robot behaviour. Without this accurate mental image, users may encounter confusion or frustration whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. The Explainable Shared Control paradigm introduced in this thesis attempts to resolve such model misalignment by jointly considering assistance and transparency. There are two perspectives of transparency to Explainable Shared Control: the human's and the robot's. Augmented reality is presented as an integral component that addresses the human viewpoint by visually unveiling the robot's internal mechanisms. Whilst the robot perspective requires an awareness of human "intent", and so a clustering framework composed of a deep generative model is developed for human intention inference. Both transparency constructs are implemented atop a real assistive robotic wheelchair and tested with human users. An augmented reality headset is incorporated into the robotic wheelchair and different interface options are evaluated across two user studies to explore their influence on mental model accuracy. Experimental results indicate that this setup facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment. As for human intention inference, the clustering framework is applied to a dataset collected from users operating the robotic wheelchair. Findings from this experiment demonstrate that the learnt clusters are interpretable and meaningful representations of human intent. This thesis serves as a first step in the interdisciplinary area of Explainable Shared Control. The contributions to shared control, augmented reality and representation learning contained within this thesis are likely to help future research advance the proposed paradigm, and thus bolster the prevalence of assistive robots.Open Acces

Human-aware space sharing and navigation for an interactive robot

Les méthodes de planification de mouvements robotiques se sont développées à un rythme accéléré ces dernières années. L'accent a principalement été mis sur le fait de rendre les robots plus efficaces, plus sécurisés et plus rapides à réagir à des situations imprévisibles. En conséquence, nous assistons de plus en plus à l'introduction des robots de service dans notre vie quotidienne, en particulier dans les lieux publics tels que les musées, les centres commerciaux et les aéroports. Tandis qu'un robot de service mobile se déplace dans l'environnement humain, il est important de prendre en compte l'effet de son comportement sur les personnes qu'il croise ou avec lesquelles il interagit. Nous ne les voyons pas comme de simples machines, mais comme des agents sociaux et nous nous attendons à ce qu'ils se comportent de manière similaire à l'homme en suivant les normes sociétales comme des règles. Ceci a créé de nouveaux défis et a ouvert de nouvelles directions de recherche pour concevoir des algorithmes de commande de robot, qui fournissent des comportements de robot acceptables, lisibles et proactifs. Cette thèse propose une méthode coopérative basée sur l'optimisation pour la planification de trajectoire et la navigation du robot avec des contraintes sociales intégrées pour assurer des mouvements de robots prudents, conscients de la présence de l'être humain et prévisibles. La trajectoire du robot est ajustée dynamiquement et continuellement pour satisfaire ces contraintes sociales. Pour ce faire, nous traitons la trajectoire du robot comme une bande élastique (une construction mathématique représentant la trajectoire du robot comme une série de positions et une différence de temps entre ces positions) qui peut être déformée (dans l'espace et dans le temps) par le processus d'optimisation pour respecter les contraintes données. De plus, le robot prédit aussi les trajectoires humaines plausibles dans la même zone d'exploitation en traitant les chemins humains aussi comme des bandes élastiques. Ce système nous permet d'optimiser les trajectoires des robots non seulement pour le moment présent, mais aussi pour l'interaction entière qui se produit lorsque les humains et les robots se croisent les uns les autres. Nous avons réalisé un ensemble d'expériences avec des situations interactives humains-robots qui se produisent dans la vie de tous les jours telles que traverser un couloir, passer par une porte et se croiser sur de grands espaces ouverts. La méthode de planification coopérative proposée se compare favorablement à d'autres schémas de planification de la navigation à la pointe de la technique. Nous avons augmenté le comportement de navigation du robot avec un mouvement synchronisé et réactif de sa tête. Cela permet au robot de regarder où il va et occasionnellement de détourner son regard vers les personnes voisines pour montrer que le robot va éviter toute collision possible avec eux comme prévu par le planificateur. À tout moment, le robot pondère les multiples critères selon le contexte social et décide de ce vers quoi il devrait porter le regard. Grâce à une étude utilisateur en ligne, nous avons montré que ce mécanisme de regard complète efficacement le comportement de navigation ce qui améliore la lisibilité des actions du robot. Enfin, nous avons intégré notre schéma de navigation avec un système de supervision plus large qui peut générer conjointement des comportements du robot standard tel que l'approche d'une personne et l'adaptation de la vitesse du robot selon le groupe de personnes que le robot guide dans des scénarios d'aéroport ou de musée.The methods of robotic movement planning have grown at an accelerated pace in recent years. The emphasis has mainly been on making robots more efficient, safer and react faster to unpredictable situations. As a result we are witnessing more and more service robots introduced in our everyday lives, especially in public places such as museums, shopping malls and airports. While a mobile service robot moves in a human environment, it leaves an innate effect on people about its demeanor. We do not see them as mere machines but as social agents and expect them to behave humanly by following societal norms and rules. This has created new challenges and opened new research avenues for designing robot control algorithms that deliver human-acceptable, legible and proactive robot behaviors. This thesis proposes a optimization-based cooperative method for trajectoryplanning and navigation with in-built social constraints for keeping robot motions safe, human-aware and predictable. The robot trajectory is dynamically and continuously adjusted to satisfy these social constraints. To do so, we treat the robot trajectory as an elastic band (a mathematical construct representing the robot path as a series of poses and time-difference between those poses) which can be deformed (both in space and time) by the optimization process to respect given constraints. Moreover, we also predict plausible human trajectories in the same operating area by treating human paths also as elastic bands. This scheme allows us to optimize the robot trajectories not only for the current moment but for the entire interaction that happens when humans and robot cross each other's paths. We carried out a set of experiments with canonical human-robot interactive situations that happen in our everyday lives such as crossing a hallway, passing through a door and intersecting paths on wide open spaces. The proposed cooperative planning method compares favorably against other stat-of-the-art human-aware navigation planning schemes. We have augmented robot navigation behavior with synchronized and responsive movements of the robot head, making the robot look where it is going and occasionally diverting its gaze towards nearby people to acknowledge that robot will avoid any possible collision with them. At any given moment the robot weighs multiple criteria according to the social context and decides where it should turn its gaze. Through an online user study we have shown that such gazing mechanism effectively complements the navigation behavior and it improves legibility of the robot actions. Finally, we have integrated our navigation scheme with a broader supervision system which can jointly generate normative robot behaviors such as approaching a person and adapting the robot speed according to a group of people who the robot guides in airports or museums

Opportunities for using eye tracking technology in manufacturing and logistics: Systematic literature review and research agenda

Workers play essential roles in manufacturing and logistics. Releasing workers from routine tasks and enabling them to focus on creative, value-adding activities can enhance their performance and wellbeing, and it is also key to the successful implementation of Industry 4.0. One technology that can help identify patterns of worker-system interaction is Eye Tracking (ET), which is a non-intrusive technology for measuring human eye movements. ET can provide moment-by-moment insights into the cognitive state of the subject during task execution, which can improve our understanding of how humans behave and make decisions within complex systems. It also enables explorations of the subject’s interaction mode with the working environment. Earlier research has investigated the use of ET in manufacturing and logistics, but the literature is fragmented and has not yet been discussed in a literature review yet. This article therefore conducts a systematic literature review to explore the applications of ET, summarise its benefits, and outline future research opportunities of using ET in manufacturing and logistics. We first propose a conceptual framework to guide our study and then conduct a systematic literature search in scholarly databases, obtaining 71 relevant papers. Building on the proposed framework, we systematically review the use of ET and categorize the identified papers according to their application in manufacturing (product development, production, quality inspection) and logistics. Our results reveal that ET has several use cases in the manufacturing sector, but that its application in logistics has not been studied extensively so far. We summarize the benefits of using ET in terms of process performance, human performance, and work environment and safety, and also discuss the methodological characteristics of the ET literature as well as typical ET measures used. We conclude by illustrating future avenues for ET research in manufacturing and logistics

An adaptable fuzzy-based model for predicting link quality in robot networks.

It is often essential for robots to maintain wireless connectivity with other systems so that commands, sensor data, and other situational information can be exchanged. Unfortunately, maintaining sufficient connection quality between these systems can be problematic. Robot mobility, combined with the attenuation and rapid dynamics associated with radio wave propagation, can cause frequent link quality (LQ) issues such as degraded throughput, temporary disconnects, or even link failure. In order to proactively mitigate such problems, robots must possess the capability, at the application layer, to gauge the quality of their wireless connections. However, many of the existing approaches lack adaptability or the framework necessary to rapidly build and sustain an accurate LQ prediction model. The primary contribution of this dissertation is the introduction of a novel way of blending machine learning with fuzzy logic so that an adaptable, yet intuitive LQ prediction model can be formed. Another significant contribution includes the evaluation of a unique active and incremental learning framework for quickly constructing and maintaining prediction models in robot networks with minimal sampling overhead