Ease-off based compensation of tooth surface deviations for spiral bevel and hypoid gears: only the pinion needs corrections

This paper presents a novel methodology to restore the designed functional properties of hypoid gear sets whose teeth deviate from their theoretical models due to inevitable imperfections in the machining process. Corrective actions are applied to one member only: the pinion. The concept of ease-off is profitably employed as the true means to evaluate the contact properties of a gear set as a whole. It is indeed the sameness of the designed and the real ease-off that ultimately renders two gear sets equivalent in terms of contact pattern, transmission error and vibrational properties. On this basis, gear deviations can be mapped into equivalent pinion deviations, added to those of the pinion itself, and cumulatively compensated for by applying corrective machine-tool settings to the pinion. The gear member is perfect “as is”. The ensuing advantages are highlighted in the paper. The method is illustrated with a real-life numerical example. It demonstrates that, applying corrective (i) machine-tool settings and (ii) machine settings only to the pinion grinding process, the originally designed transmission properties can be restored with a high level of accuracy

Grasp planning with soft hands using Bounding Box object decomposition

In this paper, we present a method to plan grasps for soft hands. Considering that soft hands can easily conform to the shape an the object, with preference to certain types of basic geometries and dimensions, we decompose the object into one type of these geometries, particularly into Minimal Volume Bounding Boxes (MVBBs), which are proved to be efficiently graspable by the hand we use. A set of hand poses are then generated using geometric information extracted from such MVBBs. All hand postures are used in a dynamic simulator of the PISA/IIT Soft Hand and put on a test to evaluate if a proposed hand posture leads to a successful grasp. We show, through a set of numerical simulations, that the probability of success of the hand poses generated with the proposed algorithm is very good and represents an evident improvement with respect to our previous results published in [1]

Grasp compliance regulation in synergistically controlled robotic hands with VSA

In this paper, we propose a general method to achieve a desired grasp compliance acting both on the joint stiffness values and on the hand configuration, also in the presence of restrictions caused by synergistic underactuation. The approach is based on the iterative exploration of the equilibrium manifold of the system and the quasi-static analysis of the governing equations. As a result, the method can cope with large commanded variations of the grasp stiffness with respect to an initial configuration. Two numerical examples are illustrated. In the first one, a simple 2D hand is analyzed so that the obtained results can be easily verified and discussed. In the second one, to show the method at work in a more realistic scenario, we model grasp compliance regulation for a DLR/HIT hand II grasping a ball

Grasping with Soft Hands

Despite some prematurely optimistic claims, the ability of robots to grasp general objects in unstructured environments still remains far behind that of humans. This is not solely caused by differences in the mechanics of hands: indeed, we show that human use of a simple robot hand (the Pisa/IIT SoftHand) can afford capabilities that are comparable to natural grasping. It is through the observation of such human-directed robot hand operations that we realized how fundamental in everyday grasping and manipulation is the role of hand compliance, which is used to adapt to the shape of surrounding objects. Objects and environmental constraints are in turn used to functionally shape the hand, going beyond its nominal kinematic limits by exploiting structural softness. In this paper, we set out to study grasp planning for hands that are simple - in the sense of low number of actuated degrees of freedom (one for the Pisa/IIT SoftHand) - but are soft, i.e. continuously deformable in an infinity of possible shapes through interaction with objects. After general considerations on the change of paradigm in grasp planning that this setting brings about with respect to classical rigid multi-dof grasp planning, we present a procedure to extract grasp affordances for the Pisa/IIT SoftHand through physically accurate numerical simulations. The selected grasps are then successfully tested in an experimental scenario

A twist exponential approach to gear generation with general spatial motions

This paper presents a novel formulation of gear generation with general spatial motions obtained by parameterizing the computer numerically controlled (CNC) machine employed for cutting via the product-of-exponentials (POE) formula. By exploiting its interpretation as a serial kinematic chain, a systematic methodology is presented for efficiently computing the forward kinematics of the generating tool relative to the gear blank, which is essential, e.g., in the derivation of the envelope (tooth) surface. A key feature of the proposed method is that elementary motions allowed by the machine joints are parameterized directly via twist exponentials, with the twofold advantage of: (1) avoiding the introduction of a long chain of reference frames; (2) obtaining the rigid-body velocity (twist) of the enveloping motion directly from the parametrization. A layout typical of the face-milling process for hypoid gears is chosen as a paradigm in the unfolding of the theory, although the approach can be adapted to any process and arrangement. As examples of application, explicit expressions pertinent to gear generation between two fixed axes, and for a set-up typical of a 9-axis Gleason CNC universal motion concept (UMC) machine are presented

A pnh-Adaptive Refinement Procedure for Numerical Optimal Control Problems

This paper presents an automatic procedure to enhance the accuracy of the numerical solution of an optimal control problem (OCP) discretized via direct collocation at Gauss–Legendre points. First, a numerical solution is obtained by solving a nonlinear program (NLP). Then, the method evaluates its accuracy and adaptively changes both the degree of the approximating polynomial within each mesh interval and the number of mesh intervals until a prescribed accuracy is met. The number of mesh intervals is increased for all state vector components alike, in a classical fashion. Instead, improving on state-of-the-art procedures, the degrees of the polynomials approximating the different components of the state vector are allowed to assume, in each finite element, distinct values. This explains the pnh definition, where n is the state dimension. With respect to the approaches found in the literature, where the degree is always raised to the highest order for all the state components, our methods allow a sensible reduction of the overall number of variables of the resulting NLP, with a corresponding reduction of the computational burden. Numerical tests on three OCP problems highlight that, under the same maximum allowable error, by independently selecting the degree of the polynomial for each state, our method effectively picks lower degrees for some of the states, thus reducing the overall number of variables in the NLP. Accordingly, various advantages are brought about, the most remarkable being: (i) an increased computational efficiency for the final enhanced mesh with solution accuracy still within the prescribed tolerance, (ii) a reduced risk of being trapped by local minima due to the reduced NLP size, and (iii) a gain of the robustness of the convergence process due to the better-behaved solution landscapes

Modeling Natural and Artificial Hands with Synergies

We report on recent work in modelling the process of grasping and active touch by natural and artificial hands. Starting from observations made in human hands about the correlation of degrees of freedom in patterns of more frequent use (postural synergies), we consider the implications of a geometrical model accounting for such data, which is applicable to the pre-grasping phase occurring when shaping the hand before actual contact with the grasped object. To extend applicability of the synergy model to study force distribution in the actual grasp, we introduce a modified model including the mechanical compliance of the hand’s musculotendinous system. Numerical results obtained by this model indicate that the same principal synergies observed from pre-grasp postural data are also fundamental in achieving proper grasp force distribution. To illustrate the concept of synergies in the dual domain of haptic sensing, we provide a review of models of how the complexity and heterogeneity of sensory information from touch can be harnessed in simplified, tractable abstractions. These abstractions are amenable to fast processing to enable quick reflexes as well as elaboration of high-level percepts. Applications of the synergy model to the design and control of artificial hands and tactile sensors are illustrated