Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection

The successful implementation of vision-based navigation in agricultural fields hinges upon two critical components: 1) the accurate identification of key components within the scene, and 2) the identification of lanes through the detection of boundary lines that separate the crops from the traversable ground. We propose Agronav, an end-to-end vision-based autonomous navigation framework, which outputs the centerline from the input image by sequentially processing it through semantic segmentation and semantic line detection models. We also present Agroscapes, a pixel-level annotated dataset collected across six different crops, captured from varying heights and angles. This ensures that the framework trained on Agroscapes is generalizable across both ground and aerial robotic platforms. Codes, models and dataset will be released at \href{https://github.com/shivamkumarpanda/agronav}{github.com/shivamkumarpanda/agronav}

A Fully Implicit Method for Robust Frictional Contact Handling in Elastic Rods

Accurate frictional contact is critical in simulating the assembly of rod-like structures in the practical world, such as knots, hairs, flagella, and more. Due to their high geometric nonlinearity and elasticity, rod-on-rod contact remains a challenging problem tackled by researchers in both computational mechanics and computer graphics. Typically, frictional contact is regarded as constraints for the equations of motions of a system. Such constraints are often computed independently at every time step in a dynamic simulation, thus slowing down the simulation and possibly introducing numerical convergence issues. This paper proposes a fully implicit penalty-based frictional contact method, Implicit Contact Model (IMC), that efficiently and robustly captures accurate frictional contact responses. We showcase our algorithm's performance in achieving visually realistic results for the challenging and novel contact scenario of flagella bundling in fluid medium, a significant phenomenon in biology that motivates novel engineering applications in soft robotics. In addition to this, we offer a side-by-side comparison with Incremental Potential Contact (IPC), a state-of-the-art contact handling algorithm. We show that IMC possesses comparable performance to IPC while converging at a faster rate.Comment: * Equal contribution. A video summarizing this work is available on YouTube: https://youtu.be/g0rlCFfWJ8

Deep Learning of Force Manifolds from the Simulated Physics of Robotic Paper Folding

Robotic manipulation of slender objects is challenging, especially when the induced deformations are large and nonlinear. Traditionally, learning-based control approaches, such as imitation learning, have been used to address deformable material manipulation. These approaches lack generality and often suffer critical failure from a simple switch of material, geometric, and/or environmental (e.g., friction) properties. This article tackles a fundamental but difficult deformable manipulation task: forming a predefined fold in paper with only a single manipulator. A data-driven framework combining physically-accurate simulation and machine learning is used to train a deep neural network capable of predicting the external forces induced on the manipulated paper given a grasp position. We frame the problem using scaling analysis, resulting in a control framework robust against material and geometric changes. Path planning is then carried out over the generated "neural force manifold" to produce robot manipulation trajectories optimized to prevent sliding, with offline trajectory generation finishing 15$\times$ faster than previous physics-based folding methods. The inference speed of the trained model enables the incorporation of real-time visual feedback to achieve closed-loop sensorimotor control. Real-world experiments demonstrate that our framework can greatly improve robotic manipulation performance compared to state-of-the-art folding strategies, even when manipulating paper objects of various materials and shapes.Comment: Supplementary video is available on YouTube: https://youtu.be/k0nexYGy-P

Design of Bistable Soft Deployable Structures via a Kirigami-inspired Planar Fabrication Approach

Fully soft bistable mechanisms have shown extensive applications ranging from soft robotics, wearable devices, and medical tools, to energy harvesting. However, the lack of design and fabrication methods that are easy and potentially scalable limits their further adoption into mainstream applications. Here a top-down planar approach is presented by introducing Kirigami-inspired engineering combined with a pre-stretching process. Using this method, Kirigami-Pre-stretched Substrate-Kirigami trilayered precursors are created in a planar manner; upon release, the strain mismatch -- due to the pre-stretching of substrate -- between layers would induce an out-of-plane buckling to achieve targeted three dimensional (3D) bistable structures. By combining experimental characterization, analytical modeling, and finite element simulation, the effect of the pattern size of Kirigami layers and pre-stretching on the geometry and stability of resulting 3D composites is explored. In addition, methods to realize soft bistable structures with arbitrary shapes and soft composites with multistable configurations are investigated, which could encourage further applications. Our method is demonstrated by using bistable soft Kirigami composites to construct two soft machines: (i) a bistable soft gripper that can gently grasp delicate objects with different shapes and sizes and (ii) a flytrap-inspired robot that can autonomously detect and capture objects

Sim2Real Neural Controllers for Physics-based Robotic Deployment of Deformable Linear Objects

Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task -- accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots.Comment: YouTube video: https://youtu.be/OSD6dhOgyMA?feature=share

Editorial: materials, design, modeling and control of soft robotic artificial muscles.

Published versio

The Epidemiology of Migraine Headache in Arab Countries: A Systematic Review

Background. Recurring migraine disorders are a common medical problem, standing among the top causes of disability and sufferings. This study aimed to evaluate epidemiological evidence to report updated estimates on prevalence, risk factors, and associated comorbidities of migraine headache in the Arab countries. Design and Setting. A systematic review was conducted at the College of Public Health and Health Informatics, Riyadh, Saudi Arabia. Methods. A systematic search in electronic databases, such as PubMed and Embase, as well as manual searches with cross-referencing was performed from 1990 up to 2019. Overall, 23 included papers were rated independently by two reviewers. Studies were eligible for inclusion only if they investigated migraine headache epidemiology in any Arab country and were published in English. Results. Migraine prevalence among the general population ranged between 2.6% and 32%. The estimated prevalence of migraine headache among medical university students ranged between 12.2% and 27.9% and between 7.1% and 13.7% in schoolchildren (6 to 18 years). Females were found more likely to have migraine than males. The duration of migraine attacks became shorter with increasing age, while chronic (daily) migraine showed increasing prevalence with age. The most commonly reported comorbidities with migraine included anxiety, hypertension, irritable bowel syndrome, and depression. Most common headache-triggering factors included stress, fatigue, sleep disturbances, prolonged exposure to excessive sunlight or heat, and hunger. Conclusion. The prevalence and risk factors of migraine headache in Arab countries are comparable to reports from western countries. Longitudinal studies are still needed to investigate the prognosis and predictors of chronicity in the arab countries

Assessment of a novel biliary-specific near-infrared fluorescent dye (BL-760) for intraoperative detection of bile ducts and biliary leaks during hepatectomy in a preclinical swine model

OBJECTIVES: Postoperative bile leakage is a common complication of hepatobiliary surgery and frequently requires procedural intervention. Bile-label 760 (BL-760), a novel near-infrared dye, has emerged as a promising tool for identifying biliary structures and leakage, owing to its rapid excretion and strong bile specificity. This study aimed to assess the intraoperative detection of biliary leakage using intravenously administered BL-760 compared with intravenous (IV) and intraductal (ID) indocyanine green (ICG). MATERIALS AND METHODS: Laparotomy and segmental hepatectomy with vascular control were performed on two 25-30 kg pigs. ID ICG, IV ICG, and IV BL-760 were administered separately, followed by an examination of the liver parenchyma, cut liver edge, and extrahepatic bile ducts for areas of leakage. The duration of intra- and extrahepatic fluorescence detection was assessed, and the target-to-background (TBR) of the bile ducts to the liver parenchyma was quantitatively measured. RESULTS: In Animal 1, after intraoperative BL-760 injection, three areas of leaking bile were identified within 5 min on the cut liver edge with a TBR of 2.5-3.8 that was not apparent to the naked eye. In contrast, after IV ICG administration, the background parenchymal signal and bleeding obscured the areas of bile leakage. A second dose of BL-760 demonstrated the utility of repeated injections, confirming two of the three previously visualized areas of bile leakage and revealing one previously unseen leak. In Animal 2, neither ID ICG nor IV BL-760 injections showed obvious areas of bile leakage. However, fluorescence signals were observed within the superficial intrahepatic bile ducts after both injections. CONCLUSIONS: BL-760 enables the rapid intraoperative visualization of small biliary structures and leaks, with the benefits of fast excretion, repeatable intravenous administration, and high-fluorescence TBR in the liver parenchyma. Potential applications include the identification of bile flow in the portal plate, biliary leak or duct injury, and postoperative monitoring of drain output. A thorough assessment of the intraoperative biliary anatomy could limit the need for postoperative drain placement, a possible contributor to severe complications and postoperative bile leak

The Epidemiology of Celiac Disease in the General Population and High-Risk Groups in Arab Countries: A Systematic Review

Background and Aims. Celiac disease (CD) is possibly the most common autoimmune disorder, which may lead to dietary problems in the Arab region. This paper is aimed at exploring the epidemiology of the celiac disease in Arab countries, including its prevalence, associated risk factors, and clinical patterns. Methods. An extensive search of the literature was conducted from electronic databases such as PubMed, Embase, and Google Scholar. In total, 134 research papers were retrieved. We extracted studies published from January 1996 to December 2019. Our search was limited to studies published in English. Findings. The review included 35 studies with 22,340 participants from 12 countries and demonstrated a wide variation in the prevalence of CD. The highest prevalence among the general population (3.2%) was reported in Saudi Arabia, and the lowest (0.1%) was reported in Tunisia. Women demonstrated a higher prevalence of celiac disease relative to men. The peak age at diagnosis fell between 1 and 3 years and 9-10 years. Most studies focused on type 1 diabetes. Children with type 1 diabetes have a higher prevalence of CD (range from 5.5% to 20%), while the prevalence of CD in Down’s syndrome patients was 1.1% and 10.7% in UAE and Saudi Arabia, respectively. Other autoimmune diseases associated with CD are thyroid disease and irritable bowel disease. The most widely recognized clinical presentation was an inability to flourish and poor weight gain, followed by short stature, abdominal pain, abdominal distension, bloating, and chronic diarrhea. Conclusion. The prevalence of the celiac disease in Arab countries varies with sex and age. However, we found that celiac disease presented similar clinical characteristics independent of the geographic region. Longitudinal population-based studies are needed to better identify the true burden and determinants of celiac disease