Surface patch reconstruction by touching

This thesis studies the reconstruction of unknown curved surfaces in 3D through contour tracking. The implementation involves a 2-axis joystick sensor and a 4-DOF Adept robot. The joystick\u27s force sensing is combined with the Adept\u27s high positional accuracy to yield precise contact measurements.;A surface patch in 3D can be rebuilt by tracking along three concurrent curves on the surface. These data curves lie in different planes and are acquired via planar contour tracking. The Darboux frame at the curve intersection is first estimated to reflect the local geometry. Then polynomial fitting is carried out in this frame. Minimization of the total (absolute) Gaussian curvature of the surface fit effectively prevents unnecessary folding otherwise expected to result from the use of touching data. Experiments have demonstrated high accuracy of reconstruction

Model-based recognition of curves and surfaces using tactile data

Model-based object recognition has mostly been studied over inputs including images and range data. Though such data are global, cameras and range sensors are subject to occlusions and clutters, which often make recognition difficult and computationally expensive. In contrast, touch by a robot hand is free of occlusion and clutter issues, and recognition over tactile data can be more efficient.;In this thesis, we investigate model-based recognition of two and three dimensional curved objects from tactile data. The recognition of 2D objects is an invariant-based approach. We have derived differential and semi-differential invariants for quadratic curves and special cubic curves that are found in applications. These invariants, independent of translation and rotation, can be computed from local geometry of a curve. Invariants for quadratic curves are the functions in terms of the curvature and its derivative with respect to arc length. For cubic curves, the derived invariants also involve a slope in their expressions. Recognition of a curve reduces to invariant verification with its canonical parametric form determined along the way. In addition, the contact locations with the robot hand are found on the curve, thereby localizing it relative to the touch sensor. We have verified the correctness of all invariants by simulations. We have also shown that the shape parameters of the recognized curve can be recovered with small errors. The byproduct is a procedure that reliably estimates curvature and its derivative from real tactile data. The presented work distinguishes itself from traditional model-based recognition in its ability to simultaneously recognize and localize a shape from one of several classes, each consisting of a continuum of shapes, by the use of local data.;The recognition of 3D objects is based on registration and consists of two steps. First, a robotic hand with touch sensors samples data points on the object\u27s surface along three concurrent curves. The two principal curvatures at the curve intersection point are estimated and then used in a table lookup to find surface points that have similar local geometries. Next, starting at each such point, a local search is conducted to superpose the tactile data onto the surface model. Recognition of the model is based on the quality of this registration. The presented method can recognize algebraic as well as free-form surfaces, as demonstrated via simulations and robot experiments. One difference in the recognition of these two sets of shapes lies in the principal curvature estimation, which are calculated from the close forms and estimated through fitting, respectively. The other difference lies in data registration, which is carried out by nonlinear optimization and a greedy algorithm, respectively

Sensing and Control for Robust Grasping with Simple Hardware

Robots can move, see, and navigate in the real world outside carefully structured factories, but they cannot yet grasp and manipulate objects without human intervention. Two key barriers are the complexity of current approaches, which require complicated hardware or precise perception to function effectively, and the challenge of understanding system performance in a tractable manner given the wide range of factors that impact successful grasping. This thesis presents sensors and simple control algorithms that relax the requirements on robot hardware, and a framework to understand the capabilities and limitations of grasping systems.Engineering and Applied Science

Optimal field coverage path planning on 2D and 3D surfaces

With the rapid adoption of automatic guidance systems, automated path planning has great potential to further optimize field operations. Field operations should be done in a manner that minimizes time, travel over field surfaces and is coordinated with specific field operations, machine characteristics and topographical features of arable lands. To reach this goal, intelligent coverage path planning algorithm is key. This dissertation documents our innovative research in optimal field coverage path planning on both 2D and 3D surfaces. To determine the full coverage pattern of a given 2D planar field by using boustrophedon paths, it is necessary to know whether to and how to decompose a field into sub-regions and how to determine the travel direction within each sub-region. A geometric model was developed to represent this coverage path planning problem, and a path planning algorithm was developed based on this geometric model. The search mechanism of the algorithm was guided by a customized cost function resulting from the analysis of different headland turning types and implemented with a divide-and-conquer strategy. The complexity of the algorithm was analyzed, and methods for reducing the computational time were discussed. Field examples with complexity ranging from a simple convex shape to an irregular polygonal shape that has multiple obstacles within its interior were tested with this algorithm. The results were compared with other reported approaches or farmers\u27 actual driving patterns. These results indicated the proposed algorithm was effective in producing optimal field decomposition and coverage path direction in each sub-region. In real world, a great proportion of farms have rolling terrains, which have considerable influences to the design of coverage paths. Coverage path planning in 3D space has a great potential to further optimize field operations. To design optimal coverage paths on 3D terrain surfaces, there were five important steps: terrain modeling and representation, topography impacts analysis, terrain decomposition and classification, coverage cost analysis and the development of optimal path searching algorithm. Each of the topics was investigated in this dissertation research. The developed algorithms and methods were successfully implemented in software and tested with practical 3D terrain farm fields with various topographical features. Each field was decomposed into sub-regions based on terrain features. An optimal seed curve was found for each sub-region and parallel coverage paths were generated by offsetting the seed curve sideways until the whole sub-region was completely covered. Compared with the 2D planning results, the experimental results of 3D coverage path planning showed its superiority in reducing both headland turning cost and soil erosion cost

Evaluation und Weiterentwicklung eines kapazitiven taktilen Näherungssensors

Die vorliegende Arbeit hat die Technologie eines kapazitiven taktilen Näherungssensors zum Thema. Zunächst wird anhand eines existierenden Sensors gezeigt, wie dieser in der Robotik in zwei Aufgabenbereichen gewinnbringend eingesetzt werden kann: in der robusten Manipulation und in der Überwachung des Umfelds des Roboters. Im Bereich der Manipulation werden zwei neue Untergebiete für diese Art von Sensoren erschlossen: die Haptische Exploration und die Telemanipulation. Dann wird diese Technologie in einem neuen Entwurf entscheidend weiterentwickelt, indem ihre Funktionalität erweitert, ihre Integrierbarkeit verbessert und ihre Ortsauflösung erhöht wird. Für den Bereich der Manipulation wird ein Zwei-Backen-Greifer mit vorhandenen Sensormodulen ausgestattet. Eine gradientenbasierte Regelung ermöglicht das berührungslose Ausrichten an Objekten in den sechs Raumfreiheitsgraden. Diese Methode ist Grundlage für die weiteren Methoden der Haptischen Exploration und der Telemanipulation. Die traditionelle Haptische Exploration wird erweitert, indem berührungslose Explorationsschritte eingeführt werden, welche effizient ausgeführt werden können. Die Telemanipulation beinhaltet, dass der Nutzer des Systems eine Kraftrückkopplung spürt, welche mit dem Gradienten, der durch die Näherungssensoren detektiert wird, korrespondiert. Mit dieser Unterstützung kann der Nutzer Objekte effizienter explorieren und greifen. Die Überwachung des Umfelds des Roboters wird realisiert, indem ein End-Effektor mit den vorhandenen Sensormodulen ausgestattet wird. In einem Szenario zur Konturverfolgung bzw. Kollisionsvermeidung wird gezeigt, dass der End-Effektor unvorhergesehene Hindernisse erfolgreich umfahren kann. Im vorgestellten Ansatz wird gezeigt, dass die geschätzte Krümmung der Hindernisfläche für eine prädiktive Regelung verwendet werden kann. Aus der anwendungsbezogenen Evaluation des Sensors werden die Anforderungen des neuen Entwurfs abgeleitet. Der Sensor wird in seiner Funktionalität erweitert, insbesondere mit der Fähigkeit, im beidseitig-kapazitiven Modus zu messen. Dieser Modus verbessert die Robustheit bei der Detektion von nicht leitenden Materialien. Hinsichtlich der Integrierbarkeit wird der Sensor modularisiert, d. h. einzelne Sensoreinheiten sind in der Lage autark zu messen und die Signale zu verarbeiten. Schließlich wird eine flexible Ortsauflösung für den Sensor realisiert, damit dieser situativ eine höhere Ortsauflösung oder eine höhere Empfindlichkeit aufweisen kann. Es wird gezeigt, dass sich die Methoden, welche für den ersten Sensor entwickelt wurden, auch mit dem neuen Sensor umsetzen lassen. Durch die bessere Integrierbarkeit und Vielseitigkeit werden die Voraussetzungen für eine weitere Verbreitung der Technologie geschaffen

Robotic Haptic Exploration of Shape and Symmetry

This thesis presents research on the use of symmetric models during haptic exploration procedures that have the objective of determining an object’s shape. These haptic exploration techniques, and their subsequent determination of a surface’s geometric properties, are crucial to allow robots to interact with a greater variety of objects, especially as the field of robotics transitions into unstructured environments. Symmetry is an extremely frequent shape property, especially in man-made objects, and it provides shape information that becomes useful in grasping and manipulation tasks, as well as enriching shape information for the aforementioned haptic exploration tasks. In this work, we present an improvement to Gaussian Process-driven exploration tasks. This method allows to describe symmetry to obtain a more precise shape estimation during active exploration, and can even be discovered in real time during the exploration procedure itself. This work involved the creation of a custom software resource to perform Gaussian Process regression with the addition of symmetries, and include a novel method of representing rotational symmetries. These novel models were then used in shape exploration procedures of 2D and 3D surfaces, both in a simulated environment and in an actual robotic task, using a series of custom-made contact sensors. These procedures are able to discover symmetry of each particular object in real time. This property can also be exploited, resulting in shape estimations that have a lower surface error and uncertainty. Additionally, exploration experiments that use these symmetry-finding procedures also require a lower total number of physical contacts and take less time to finish