On the false positives and false negatives of the Jacobian Matrix in kinematically redundant parallel mechanisms

The Jacobian matrix is a highly popular tool for the control and performance analysis of closed-loop robots. Its usefulness in parallel mechanisms is certainly apparent, and its application to solve motion planning problems, or other higher level questions, has been seldom queried, or limited to non-redundant systems. In this paper, we discuss the shortcomings of the use of the Jacobian matrix under redundancy, in particular when applied to kinematically redundant parallel architectures with non-serially connected actuators. These architectures have become fairly popular recently as they allow the end-effector to achieve full rotations, which is an impossible task with traditional topologies. The problems with the Jacobian matrix in these novel systems arise from the need to eliminate redundant variables when forming it, resulting in both situations where the Jacobian incorrectly identifies singularities (false positive), and where it fails to identify singularities (false negative). These issues have thus far remained unaddressed in the literature. We highlight these limitations herein by demonstrating several cases using numerical examples of both planar and spatial architectures

Study on Mechanical Relaxations of 7075 (Al–Zn–Mg) and 2024 (Al–Cu–Mg) Alloys by Application of the Time-Temperature Superposition Principle

The viscoelastic response of commercial Al–Zn–Mg and Al–Cu–Mg alloys was measured with a dynamic-mechanical analyzer (DMA) as a function of the temperature (from 30 to 425ºC) and the loading frequency (from 0.01 to 150 Hz). The time-temperature superposition (TTS) principle has proven to be useful in studying mechanical relaxations and obtaining master curves for amorphous materials. In this work, the TTS principle is applied to the measured viscoelastic data (i.e., the storage and loss moduli) to obtain the corresponding master curves, and to analyze the mechanical relaxations responsible for the viscoelastic behavior of the studied alloys. For the storage modulus it was possible to identify a master curve for a low-temperature region (from room temperature to 150ºC) and, for the storage and loss moduli, another master curve for a high-temperature region (from 320 to 375ºC). These temperature regions are coincidental with the stable intervals where no phase transformations occur. The different temperature dependencies of the shift factors for the identified master curves, manifested by different values of the activation energy in the Arrhenius expressions for the shift factor, are due to the occurrence of microstructural changes and variations in the relaxation mechanisms between the mentioned temperature regions.Peer ReviewedPostprint (published version

Gross motion analysis of fingertip-based within-hand manipulation

Fingertip-based within-hand manipulation, also called precision manipulation, refers to the repositioning of a grasped object within the workspace of a multi-fingered robot hand without breaking or changing the contact type between each fingertip and the object. Given a robot hand architecture and a set of assumed contact models, this paper presents a method to perform a gross motion analysis of its precision manipulation capabilities, regardless of the particularities of the object being manipulated. In particular, the technique allows the composition of the displacement manifold of the grasped object relative to the palm of the robot hand to be determined as well as the displacements that can be controlled—useful for high-level design and classification of hand function. The effects of a fingertip contacting a body in this analysis are modeled as kinematic chains composed of passive and resistant revolute joints; what permits the introduction of a general framework for the definition and classification of non-frictional and frictional contact types. Examples of the application of the proposed method in several architectures of multi-fingered hands with different contact assumptions are discussed; they illustrate how inappropriate contact conditions may lead to uncontrollable displacements of the grasped object

UniNet: Next Term Course Recommendation using Deep Learning

Course enrollment recommendation is a relevant task that helps university students decide what is the best combination of courses to enroll in the next term. In particular, recommender system techniques like matrix factorization and collaborative filtering have been developed to try to solve this problem. As these techniques fail to represent the time-dependent nature of academic performance datasets we propose a deep learning approach using recurrent neural networks that aims to better represent how chronological order of course grades affects the probability of success. We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information and that it is possible to develop a recommender system with academic student performance prediction. This is shown to be meaningful across different student GPA levels and course difficultie

Immersive Demonstrations are the Key to Imitation Learning

Achieving successful robotic manipulation is an essential step towards robots being widely used in industry and home settings. Recently, many learning-based methods have been proposed to tackle this challenge, with imitation learning showing great promise. However, imperfect demonstrations and a lack of feedback from teleoperation systems may lead to poor or even unsafe results. In this work we explore the effect of demonstrator force feedback on imitation learning, using a feedback glove and a robot arm to render fingertip-level and palm-level forces, respectively. 10 participants recorded 5 demonstrations of a pick-and-place task with 3 grippers, under conditions with no force feedback, fingertip force feedback, and fingertip and palm force feedback. Results show that force feedback significantly reduces demonstrator fingertip and palm forces, leads to a lower variation in demonstrator forces, and recorded trajectories that a quicker to execute. Using behavioral cloning, we find that agents trained to imitate these trajectories mirror these benefits, even though agents have no force data shown to them during training. We conclude that immersive demonstrations, achieved with force feedback, may be the key to unlocking safer, quicker to execute dexterous manipulation policies.Comment: This paper is accepted to be presented on IEEE International Conference on Robotics and Automation (ICRA) 202

Innovative NDT technique for detection of surface cracks based on ferrofluids excited with DC and AC magnetic fields

Innovative NDT technique for detection of surface cracks based on ferrofluids excited with DC and AC magnetic fieldsAn innovative NDT technique is proposed for surface inspection of materials not necessarily magnetic or conductive, based on local magnetic field variations due to ferrofluid deposited in the cracks. The feasibility of the technique is assessed preliminarily, based on signal detectability without applied external magnetic field, and under applied DC and AC fields. The signals are quantified analytically, experimentally and numerically. In DC, detection is based on local magnetic flux density variations. In AC, detection is based on the existing phase lag between the field close to the crack and the applied field. This approach has inherent advantages: the phase lag, as opposed to the magnetic flux density, is independent of the quantity of ferrofluid in the crack and the magnitude of the applied field. The model agrees well with the tests, showing that the signal increases with the applied field strength, up to the saturation magnetization of the ferrofluid, and decreases with the distance to the crack longitudinal axis, and thus it can provide useful estimations of the signal. The proposed technique, requiring application of external fields to magnetize the ferrofluid to enhance the signal, seems promising: the model suggests that signals associated to cracks significantly smaller than the minimum detectable surface cracks for comparable classical NDT techniques are easily detectable with commercial magnetometers.Postprint (published version

The GR2 gripper: an underactuated hand for open-loop in-hand planar manipulation

Performing dexterous manipulation of unknown objects with robot grippers without using high-fidelity contact sensors, active/sliding surfaces, or a priori workspace exploration is still an open problem in robot manipulation and a necessity for many robotics applications. In this paper, we present a two-fingered gripper topology that enables an enhanced predefined in-hand manipulation primitive controlled without knowing the size, shape, or other particularities of the grasped object. The in-hand manipulation behavior, namely, the planar manipulation of the grasped body, is predefined thanks to a simple hybrid low-level control scheme and has an increased range of motion due to the introduction of an elastic pivot joint between the two fingers. Experimental results with a prototype clearly show the advantages and benefits of the proposed concept. Given the generality of the topology and in-hand manipulation principle, researchers and designers working on multiple areas of robotics can benefit from the findings