Two-Finger 3D Rotations for Novice Users: Surjective and Integral Interactions

International audienceNow that 3D interaction is available on tablets and smart phones, it becomes critical to provide efficient 3D interaction techniques for novice users. This paper investigates interaction techniques for 3D rotation with two fingers of a single hand, on multitouch mobile devices. We introduce two new rotation techniques that allow integral control of the 3 axes of rotation. These techniques also satisfy a new criterion that we introduce: surjection. We ran a study to compare the new techniques with two widely used rotation techniques from the literature. Results indicate that surjection and integration lead to a performance improvement of a group of participants who had no prior experience in 3D interaction. Qualitative results also indicate participants' preference for the new interaction techniques

Survey on assembly sequencing: a combinatorial and geometrical perspective

A systematic overview on the subject of assembly sequencing is presented. Sequencing lies at the core of assembly planning, and variants include finding a feasible sequence—respecting the precedence constraints between the assembly operations—, or determining an optimal one according to one or several operational criteria. The different ways of representing the space of feasible assembly sequences are described, as well as the search and optimization algorithms that can be used. Geometry plays a fundamental role in devising the precedence constraints between assembly operations, and this is the subject of the second part of the survey, which treats also motion in contact in the context of the actual performance of assembly operations.Peer ReviewedPostprint (author’s final draft

Machine vision based teleoperation aid

When teleoperating a robot using video from a remote camera, it is difficult for the operator to gauge depth and orientation from a single view. In addition, there are situations where a camera mounted for viewing by the teleoperator during a teleoperation task may not be able to see the tool tip, or the viewing angle may not be intuitive (requiring extensive training to reduce the risk of incorrect or dangerous moves by the teleoperator). A machine vision based teleoperator aid is presented which uses the operator's camera view to compute an object's pose (position and orientation), and then overlays onto the operator's screen information on the object's current and desired positions. The operator can choose to display orientation and translation information as graphics and/or text. This aid provides easily assimilated depth and relative orientation information to the teleoperator. The camera may be mounted at any known orientation relative to the tool tip. A preliminary experiment with human operators was conducted and showed that task accuracies were significantly greater with than without this aid

Basic set of behaviours for programming assembly robots

We know from the well established Church-Turing thesis that any computer program­ming language needs just a limited set of commands in order to perform any computable process. However, programming in these terms is so very inconvenient that a larger set of machine codes need to be introduced and on top of these higher programming languages are erected.In Assembly Robotics we could theoretically formulate any assembly task, in terms of moves. Nevertheless, it is as tedious and error prone to program assemblies at this low level as it would be to program a computer by using just Turing Machine commands.An interesting survey carried out in the beginning of the nineties showed that the most common assembly operations in manufacturing industry cluster in just seven classes. Since the research conducted in this thesis is developed within the behaviour-based assembly paradigm which views every assembly task as the external manifestation of the execution of a behavioural module, we wonder whether there exists a limited and ergonomical set of elementary modules with which to program at least 80% of the most common operations.IIn order to investigate such a problem, we set a project in which, taking into account the statistics of the aforementioned survey, we analyze the experimental behavioural decomposition of three significant assembly tasks (two similar benchmarks, the STRASS assembly, and a family of torches). From these three we establish a basic set of such modules.The three test assemblies with which we ran the experiments can not possibly exhaust ah the manufacturing assembly tasks occurring in industry, nor can the results gathered or the speculations made represent a theoretical proof of the existence of the basic set. They simply show that it is possible to formulate different assembly tasks in terms of a small set of about 10 modules, which may be regarded as an embryo of a basic set of elementary modules.Comparing this set with Kondoleon’s tasks and with Balch’s general-purpose robot routines, we observed that ours was general enough to represent 80% of the most com­mon manufacturing assembly tasks and ergonomical enough to be easily used by human operators or automatic planners. A final discussion shows that it would be possible to base an assembly programming language on this kind of set of basic behavioural modules

Enhanced device-based 3D object manipulation technique for handheld mobile augmented reality

3D object manipulation is one of the most important tasks for handheld mobile Augmented Reality (AR) towards its practical potential, especially for realworld assembly support. In this context, techniques used to manipulate 3D object is an important research area. Therefore, this study developed an improved device based interaction technique within handheld mobile AR interfaces to solve the large range 3D object rotation problem as well as issues related to 3D object position and orientation deviations in manipulating 3D object. The research firstly enhanced the existing device-based 3D object rotation technique with an innovative control structure that utilizes the handheld mobile device tilting and skewing amplitudes to determine the rotation axes and directions of the 3D object. Whenever the device is tilted or skewed exceeding the threshold values of the amplitudes, the 3D object rotation will start continuously with a pre-defined angular speed per second to prevent over-rotation of the handheld mobile device. This over-rotation is a common occurrence when using the existing technique to perform large-range 3D object rotations. The problem of over-rotation of the handheld mobile device needs to be solved since it causes a 3D object registration error and a 3D object display issue where the 3D object does not appear consistent within the user’s range of view. Secondly, restructuring the existing device-based 3D object manipulation technique was done by separating the degrees of freedom (DOF) of the 3D object translation and rotation to prevent the 3D object position and orientation deviations caused by the DOF integration that utilizes the same control structure for both tasks. Next, an improved device-based interaction technique, with better performance on task completion time for 3D object rotation unilaterally and 3D object manipulation comprehensively within handheld mobile AR interfaces was developed. A pilot test was carried out before other main tests to determine several pre-defined values designed in the control structure of the proposed 3D object rotation technique. A series of 3D object rotation and manipulation tasks was designed and developed as separate experimental tasks to benchmark both the proposed 3D object rotation and manipulation techniques with existing ones on task completion time (s). Two different groups of participants aged 19-24 years old were selected for both experiments, with each group consisting sixteen participants. Each participant had to complete twelve trials, which came to a total 192 trials per experiment for all the participants. Repeated measure analysis was used to analyze the data. The results obtained have statistically proven that the developed 3D object rotation technique markedly outpaced existing technique with significant shorter task completion times of 2.04s shorter on easy tasks and 3.09s shorter on hard tasks after comparing the mean times upon all successful trials. On the other hand, for the failed trials, the 3D object rotation technique was 4.99% more accurate on easy tasks and 1.78% more accurate on hard tasks in comparison to the existing technique. Similar results were also extended to 3D object manipulation tasks with an overall 9.529s significant shorter task completion time of the proposed manipulation technique as compared to the existing technique. Based on the findings, an improved device-based interaction technique has been successfully developed to address the insufficient functionalities of the current technique

Real-time 3D hand reconstruction in challenging scenes from a single color or depth camera

Hands are one of the main enabling factors for performing complex tasks and humans naturally use them for interactions with their environment. Reconstruction and digitization of 3D hand motion opens up many possibilities for important applications. Hands gestures can be directly used for human–computer interaction, which is especially relevant for controlling augmented or virtual reality (AR/VR) devices where immersion is of utmost importance. In addition, 3D hand motion capture is a precondition for automatic sign-language translation, activity recognition, or teaching robots. Different approaches for 3D hand motion capture have been actively researched in the past. While being accurate, gloves and markers are intrusive and uncomfortable to wear. Hence, markerless hand reconstruction based on cameras is desirable. Multi-camera setups provide rich input, however, they are hard to calibrate and lack the flexibility for mobile use cases. Thus, the majority of more recent methods uses a single color or depth camera which, however, makes the problem harder due to more ambiguities in the input. For interaction purposes, users need continuous control and immediate feedback. This means the algorithms have to run in real time and be robust in uncontrolled scenes. These requirements, achieving 3D hand reconstruction in real time from a single camera in general scenes, make the problem significantly more challenging. While recent research has shown promising results, current state-of-the-art methods still have strong limitations. Most approaches only track the motion of a single hand in isolation and do not take background-clutter or interactions with arbitrary objects or the other hand into account. The few methods that can handle more general and natural scenarios run far from real time or use complex multi-camera setups. Such requirements make existing methods unusable for many aforementioned applications. This thesis pushes the state of the art for real-time 3D hand tracking and reconstruction in general scenes from a single RGB or depth camera. The presented approaches explore novel combinations of generative hand models, which have been used successfully in the computer vision and graphics community for decades, and powerful cutting-edge machine learning techniques, which have recently emerged with the advent of deep learning. In particular, this thesis proposes a novel method for hand tracking in the presence of strong occlusions and clutter, the first method for full global 3D hand tracking from in-the-wild RGB video, and a method for simultaneous pose and dense shape reconstruction of two interacting hands that, for the first time, combines a set of desirable properties previously unseen in the literature.Hände sind einer der Hauptfaktoren für die Ausführung komplexer Aufgaben, und Menschen verwenden sie auf natürliche Weise für Interaktionen mit ihrer Umgebung. Die Rekonstruktion und Digitalisierung der 3D-Handbewegung eröffnet viele Möglichkeiten für wichtige Anwendungen. Handgesten können direkt als Eingabe für die Mensch-Computer-Interaktion verwendet werden. Dies ist insbesondere für Geräte der erweiterten oder virtuellen Realität (AR / VR) relevant, bei denen die Immersion von größter Bedeutung ist. Darüber hinaus ist die Rekonstruktion der 3D Handbewegung eine Voraussetzung zur automatischen Übersetzung von Gebärdensprache, zur Aktivitätserkennung oder zum Unterrichten von Robotern. In der Vergangenheit wurden verschiedene Ansätze zur 3D-Handbewegungsrekonstruktion aktiv erforscht. Handschuhe und physische Markierungen sind zwar präzise, aber aufdringlich und unangenehm zu tragen. Daher ist eine markierungslose Handrekonstruktion auf der Basis von Kameras wünschenswert. Multi-Kamera-Setups bieten umfangreiche Eingabedaten, sind jedoch schwer zu kalibrieren und haben keine Flexibilität für mobile Anwendungsfälle. Daher verwenden die meisten neueren Methoden eine einzelne Farb- oder Tiefenkamera, was die Aufgabe jedoch schwerer macht, da mehr Ambiguitäten in den Eingabedaten vorhanden sind. Für Interaktionszwecke benötigen Benutzer kontinuierliche Kontrolle und sofortiges Feedback. Dies bedeutet, dass die Algorithmen in Echtzeit ausgeführt werden müssen und robust in unkontrollierten Szenen sein müssen. Diese Anforderungen, 3D-Handrekonstruktion in Echtzeit mit einer einzigen Kamera in allgemeinen Szenen, machen das Problem erheblich schwieriger. Während neuere Forschungsarbeiten vielversprechende Ergebnisse gezeigt haben, weisen aktuelle Methoden immer noch Einschränkungen auf. Die meisten Ansätze verfolgen die Bewegung einer einzelnen Hand nur isoliert und berücksichtigen keine alltäglichen Umgebungen oder Interaktionen mit beliebigen Objekten oder der anderen Hand. Die wenigen Methoden, die allgemeinere und natürlichere Szenarien verarbeiten können, laufen nicht in Echtzeit oder verwenden komplexe Multi-Kamera-Setups. Solche Anforderungen machen bestehende Verfahren für viele der oben genannten Anwendungen unbrauchbar. Diese Dissertation erweitert den Stand der Technik für die Echtzeit-3D-Handverfolgung und -Rekonstruktion in allgemeinen Szenen mit einer einzelnen RGB- oder Tiefenkamera. Die vorgestellten Algorithmen erforschen neue Kombinationen aus generativen Handmodellen, die seit Jahrzehnten erfolgreich in den Bereichen Computer Vision und Grafik eingesetzt werden, und leistungsfähigen innovativen Techniken des maschinellen Lernens, die vor kurzem mit dem Aufkommen neuronaler Netzwerke entstanden sind. In dieser Arbeit werden insbesondere vorgeschlagen: eine neuartige Methode zur Handbewegungsrekonstruktion bei starken Verdeckungen und in unkontrollierten Szenen, die erste Methode zur Rekonstruktion der globalen 3D Handbewegung aus RGB-Videos in freier Wildbahn und die erste Methode zur gleichzeitigen Rekonstruktion von Handpose und -form zweier interagierender Hände, die eine Reihe wünschenwerter Eigenschaften komibiniert