This paper studies the recognition of low-degree polynomial curves based on minimal tactile data. Euclidean differential and semi-differential invariants have been derived for quadratic curves and special cubic curves that are found in applications. These invariants, independent of translation and rotation, are evaluated over the differential geometry at up to three points on a curve. Their values are independent of the evaluation points. Recognition of the curve reduces to invariant verification with its canonical parametric form determined along the way. In addition, the contact locations are found on the curve, thereby localizing it relative to the touch sensor. Simulation results support the method despite numerical errors. Preliminary experiments have also been carried out with the introduction of a method for reliable curvature estimation. 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.
This paper studies the recognition and localization of 2-D shapes bounded by low-degree polynomial curve segments based on minimal tactile data. We have derived differential invariants for quadratic curves and two special classes of cubic curves. Such an invariant, independent of translation and rotation, is computed from the local geometry at any two points on the curve. Recognition of a curve class becomes verifying the corresponding invariant with more than one pairs of data points. Next, the actual curve is determined in its canonical parametric form using the same tactile data. Finally, the contact locations on the curve are computed, thereby localizing the shape completely relative to the touching hand. Simulation results support the working of the method in the presence of small noise, although real experiments need to be carried out in the future to demonstrate its applicability. The presented work distinguishes from traditional model-based recognition in its ability to simultaneously recognize as well as localize a shape from one of several classes, each consisting of a continuum of shapes.
This paper presents a method for recognition of 3D objects with curved surfaces from linear tactile data. For every surface model in a given database, a lookup table is constructed to store principal curvatures precomputed at points of discretization on the surface. To recognize an object, a robot hand with touch sensing capability obtains data points on its surface along three concurrent curves. The two principal curvatures estimated at the curve intersection point are used to look up the table associated with each model to locate surface discretization points that have similar local geometries. Local searches are then performed starting at these points to register the tactile data onto the model. The model with the best registration result is recognized. The presented method can recognize closed-form surfaces as well as triangular meshes, as demonstrated through simulation and robot experiments. Potential applications include recognition of (manufactured) home items that are grasped daily, and dexterous manipulation during which recognition is simultaneously performed.Note to Practitioners-In automation, the eventual purpose of sensing is for assembly and other operations. Recognition by touch has several advantages: efficiency, robustness to disturbance, and immunity to occlusion (unlike a vision system). We demonstrate that 3D objects can be recognized with small amounts of data acquired via robot touch. In particular, recognition can be as simple as dragging one or more fingers across a part of an object's surface.
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