Real-time computation of distance to dynamic obstacles with multiple depth sensors

We present an efficient method to evaluate distances between dynamic obstacles and a number of points of interests (e.g., placed on the links of a robot) when using multiple depth cameras. A depth-space oriented discretization of the Cartesian space is introduced that represents at best the workspace monitored by a depth camera, including occluded points. A depth grid map can be initialized off line from the arrangement of the multiple depth cameras, and its peculiar search characteristics allows fusing on line the information given by the multiple sensors in a very simple and fast way. The real-time performance of the proposed approach is shown by means of collision avoidance experiments where two Kinect sensors monitor a human-robot coexistence task

Utilizzo in Tempo Reale di informazioni Visive Stereo per in Controllo in Sicurezza di un Manipolatore Robotico in presenza di Operatori umani

Dato un sistema di camere Stereo montate su un manipolatore robotico antropomorfo. Si riconosce la presenza di ostacoli (es. operatore umani) all'interno dello spazio di lavoro; Quindi, con un sistema di forze respingenti basato sullo spazio delle configurazioni (C-Space), il manipolatore compie il task comandato evitando tutti gli ostacoli

Control of Redundant Robots Under Hard Joint Constraints: Saturation in the Null Space

We present an efficient method for addressing online the inversion of differential task kinematics for redundant manipulators, in the presence of hard limits on joint space motion that can never be violated. The proposed SNS (Saturation in the Null Space) algorithm proceeds by successively discarding the use of joints that would exceed their motion bounds when using the minimum norm solution. When processing multiple tasks with priority, the SNS method realizes a preemptive strategy by preserving the correct order of priority in spite of the presence of saturations. In the single- and multi-task case, the algorithm automatically integrates a least possible task scaling procedure, when an original task is found to be unfeasible. The optimality properties of the SNS algorithm are analyzed by considering an associated Quadratic Programming problem. Its solution leads to a variant of the algorithm, which guarantees optimality also when the basic SNS algorithm does not. Numerically efficient versions of these algorithms are proposed. Their performance allows real-time control of robots executing many prioritized tasks with a large number of hard bounds. Experimental results are reported

Risk factors associated with adverse fetal outcomes in pregnancies affected by Coronavirus disease 2019 (COVID-19): a secondary analysis of the WAPM study on COVID-19.

Objectives To evaluate the strength of association between maternal and pregnancy characteristics and the risk of adverse perinatal outcomes in pregnancies with laboratory confirmed COVID-19. Methods Secondary analysis of a multinational, cohort study on all consecutive pregnant women with laboratory-confirmed COVID-19 from February 1, 2020 to April 30, 2020 from 73 centers from 22 different countries. A confirmed case of COVID-19 was defined as a positive result on real-time reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay of nasal and pharyngeal swab specimens. The primary outcome was a composite adverse fetal outcome, defined as the presence of either abortion (pregnancy loss before 22 weeks of gestations), stillbirth (intrauterine fetal death after 22 weeks of gestation), neonatal death (death of a live-born infant within the first 28 days of life), and perinatal death (either stillbirth or neonatal death). Logistic regression analysis was performed to evaluate parameters independently associated with the primary outcome. Logistic regression was reported as odds ratio (OR) with 95% confidence interval (CI). Results Mean gestational age at diagnosis was 30.6+/-9.5 weeks, with 8.0% of women being diagnosed in the first, 22.2% in the second and 69.8% in the third trimester of pregnancy. There were six miscarriage (2.3%), six intrauterine device (IUD) (2.3) and 5 (2.0%) neonatal deaths, with an overall rate of perinatal death of 4.2% (11/265), thus resulting into 17 cases experiencing and 226 not experiencing composite adverse fetal outcome. Neither stillbirths nor neonatal deaths had congenital anomalies found at antenatal or postnatal evaluation. Furthermore, none of the cases experiencing IUD had signs of impending demise at arterial or venous Doppler. Neonatal deaths were all considered as prematurity-related adverse events. Of the 250 live-born neonates, one (0.4%) was found positive at RT-PCR pharyngeal swabs performed after delivery. The mother was tested positive during the third trimester of pregnancy. The newborn was asymptomatic and had negative RT-PCR test after 14 days of life. At logistic regression analysis, gestational age at diagnosis (OR: 0.85, 95% CI 0.8-0.9 per week increase; pPeer reviewe

A Reverse Priority approach to multi-task control of redundant robots

A novel method to handle multiple robotic tasks with priorities is presented. The occurrence of singularities, both of the kinematic and algorithmic type, may affect the correct hierarchy in task execution. Existing methods deal with singularities either by using damped least squares solutions or by relaxing the enforcement of secondary tasks. Damped pseudo-inversion mitigates undesired effects near singularities, at the cost of non-negligible task errors and deformation even of the highest priority task. When secondary tasks are not enforced, hierarchy is preserved but these tasks are not executed accurately even when this would be possible. In our approach, joint motion contributions are added following the reverse order of task priorities and working with suitable projection opera- tors. Higher priority tasks are processed at the end, avoiding possible deformations caused by singularities occurring in lower priority tasks. The proposed Reverse Priority (RP) method allows executing at best all tasks while still preserving the desired hierarchy. The effectiveness of the RP method is shown through numerical simulations and with experiments on a 7-dof KUKA LWR

A PD-type regulator with exact gravity cancellation for robots with flexible joints

We present a new control approach to regulation tasks for robots with elastic joints in the presence of gravity. The control law combines a term that cancels the gravity effects on the robot link dynamics with a PD-type error feedback on the motor variables. The first control component follows from the feedback equivalence principle when imposing to the link variables the same dynamic behavior as if gravity were absent. The PD component can then be designed in a rather straightforward way. Global asymptotic stability is shown via Lyapunov analysis, without the need of strictly positive lower bounds neither on the proportional control gain nor on the structural joint stiffness. The control approach is also extended to the case of robot joints with nonlinear stiffness

Residual-based stiffness estimation in robots with flexible transmissions

We propose a novel approach for estimating the nonlinear stiffness of robot joints with flexible transmissions. Based on the definition of dynamic residual signals, we derive stiffness estimation methods that use only position and velocity measurements on the motor side and needs only the knowledge of the dynamic parameters of the motors. In particular, no extra force/torque sensing is needed. Two different strategies are considered, a model-based stiffness estimator and a black-box stiffness estimator. Both strategies consist of two stages. The first stage of the model-based estimator generates a residual signal that is a first-order filtered version of the flexibility torque of the transmission, while in the second stage a least squares fitting method is used to estimate the model parameters of the stiffness. The black-box estimator uses in the first stage a second-order residual that is directly a filtered version of the stiffness multiplied by the deformation rate of the transmission. In the second stage, a simple regressor provides the transmission stiffness in a singularity-robust way. Numerical results reported for the cases of constant, nonlinear, or variable stiffness transmissions demonstrate the effectiveness of the approach and the relative merits of the two estimation strategies. © 2011 IEEE

Stiffness estimation and nonlinear control of robots with variable stiffness actuation

We consider the problem of estimating on line the nonlinear stiffness of flexible transmissions in robots with variable stiffness actuation in agonistic-antagonistic configuration. Stiffness estimation is obtained using a dynamic residual that provides a filtered version of the unmeasured flexibility torques, combining it with a recursive least squares algorithm that fits a polynomial model to the data, and proceeding then by analytical derivation. Only motor position/velocity and link position measurements are used, while knowledge of dynamic parameters is required for the motors but not for the links. The estimated stiffness function, together with its first two derivatives with respect to the deformation, is used within a feedback linearization controller designed for simultaneous tracking of desired trajectories for the links and the device stiffnesses. Simulation results provided for the VSA-II device demonstrate the perfomance of the estimation process and the effectiveness of the complete control approach. © 2011 IFAC