Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation

The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.Comment: Under review for IEEE Robotics and Automation Letter

Learning from Experience for Rapid Generation of Local Car Maneuvers

Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy. We propose to train a deep neural network (DNN) to plan feasible and nearly-optimal paths for kinematically constrained vehicles in small constant time. Our DNN model is trained using a novel weakly supervised approach and a gradient-based policy search. On real and simulated scenes and a large set of local planning problems, we demonstrate that our approach outperforms the existing planners with respect to the number of successfully completed tasks. While the path generation time is about 40 ms, the generated paths are smooth and comparable to those obtained from conventional path planners

Surgical management of atheroslerotic internal carotid artery stenosis — first own experience report from General and Vascular Surgery Ward in Siedlce Specialist Hospital

Wstęp: W latach 2009–2011 w Oddziale Chirurgii Ogólnej i Naczyniowej Mazowieckiego Szpitala Woje­wódzkiego w Siedlcach wykonano 163 operacje udrożnienia tętnic szyjnych wewnętrznych. Materiał i metody: Do zabiegu zakwalifikowano 144 (89%) pacjentów z objawowym zwężeniem tętnicy szyjnej wewnętrznej > 70% oraz 19 (11%) z bezobjawowym zwężeniem > 80%. Czasowy przepływ do­mózgowy shunt zastosowano w 9 przypadkach. Wyniki: Metodą endarterektomii z bezpośrednim zeszyciem tętnicy wykonano 142 operacje (87%). Zabieg udroż­nienia z użyciem łaty poliuretanowej przeprowadzono 21 razy (13%). Skumulowany odsetek udarów i zgonów wyniósł 1,3%. W trzech przypadkach (1,8%) stwierdzono śródoperacyjnie brak wypływu wstecznego z tętnicy szyjnej wewnętrznej, mimo że badanie USG wykonane przed operacją nie wykazało niedrożności naczynia. Wnioski: 1. Powstawanie nowych ośrodków wykonujących zabiegi udrożnienia tętnic szyjnych nie wpływa niekorzystnie na ogólnokrajowe statystyki powikłań pooperacyjnych. 2. U chorych z krytycznym zwężeniem tętnicy szyjnej wewnętrznej konieczne jest wykonanie badania ultrasonograficznego doppler-duplex bezpośred­nio przed zabiegiem endarterektomii. 3. Użycie łaty poliuretanowej, służącej do zamknięcia arteriotomii, może istotnie wpływać na zmniejszenie ryzyka wystąpienia restenozy po udrożnieniu tętnicy szyjnej wewnętrznej.Introduction: During the period 2009–2011 we performed 163 cervical endarterectomies in the General and Vascular Surgery Ward in Siedlce Specialist Hospital. Material and methods: 144 patients (89%) underwent CEA because of symptomatic ICA stenosis of more than 70%. Moreover, the remaining 19 patients (11%) had asymptomatic ICA stenosis of more than 80%. A shunt was employed generally in 9 patients. Results: CEA was performed with artery primary closure in 142 (87%) and in 21 (13%) cases using a polyu­rethane patch. Total percentage of strokes and deaths was 1.3%. In 3 cases (1.8%) there was no reverse ICA blood flow and a preoperative ultrasound examination did not show ICA occlusion. Conclusions: 1. Introducing new cervical artery revascularization centers do not negatively affect nationwide postoperative complication rates. 2. Patients with critical internal cervical artery stenosis should undergo a Doppler-duplex ultrasound examination before an endarteriectomy. 3. The use of a polyurethane patch to perform an arteriotomy closure reduces the risk of restenosis

DLOFTBs -- Fast Tracking of Deformable Linear Objects with B-splines

While manipulating rigid objects is an extensively explored research topic, deformable linear object (DLO) manipulation seems significantly underdeveloped. A potential reason for this is the inherent difficulty in describing and observing the state of the DLO as its geometry changes during manipulation. This paper proposes an algorithm for fast-tracking the shape of a DLO based on the masked image. Having no prior knowledge about the tracked object, the proposed method finds a reliable representation of the shape of the tracked object within tens of milliseconds. This algorithm's main idea is to first skeletonize the DLO mask image, walk through the parts of the DLO skeleton, arrange the segments into an ordered path, and finally fit a B-spline into it. Experiments show that our solution outperforms the State-of-the-Art approaches in DLO's shape reconstruction accuracy and algorithm running time and can handle challenging scenarios such as severe occlusions, self-intersections, and multiple DLOs in a single image.Comment: Accepted at International Conference on Robotics and Automation (ICRA) 202

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation

Tuning of extended state observer with neural network-based control performance assessment

The extended state observer (ESO) is an inherent element of robust observer-based control systems that allows estimating the impact of disturbance on system dynamics. Proper tuning of ESO parameters is necessary to ensure a good quality of estimated quantities and impacts the overall performance of the robust control structure. In this paper, we propose a neural network (NN) based tuning procedure that allows the user to prioritize between selected quality criteria such as the control and observation errors and the specified features of the control signal. The designed NN provides an accurate assessment of the control system performance and returns a set of ESO parameters that delivers a near-optimal solution to the user-defined cost function. The proposed tuning procedure, using an estimated state from the single closed-loop experiment produces near-optimal ESO gains within seconds

Active disturbance rejection control design with suppression of sensor noise effects in application to DC–DC buck power converter

The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise. In this article, this problem is addressed by transforming the high-gain extended state observer (ESO), which is the inherent element of ADRC, into a new cascade observer structure. Set of experiments, performed on a dc-dc buck power converter system, show that the new cascade ESO design, compared to the conventional approach, effectively suppresses the detrimental effect of sensor noise overamplification while increasing the estimation/control performance. The proposed design is also analyzed with a low-pass filter at the converter output, which is a common technique for reducing measurement noise in industrial applications