Physical Interaction and Control of Robotic Systems Using Hardware-in-the-Loop Simulation

Robotic systems used in industries and other complex applications need huge investment, and testing of them under robust conditions are highly challenging. Controlling and testing of such systems can be done with ease with the support of hardware-in-the-loop (HIL) simulation technique and it saves lot of time and resources. The chapter deals on the various interaction methods of robotic systems with physical environments using tactile, force, and vision sensors. It also discusses about the usage of hardware-in-the-loop technique for testing of grasp and task control algorithms in the model of robotic systems. The chapter also elaborates on usage of hardware and software platforms for implementing the control algorithms for performing physical interaction. Finally, the chapter summarizes with the case study of HIL implementation of the control algorithms in Texas Instruments (TI) C2000 microcontroller, interacting with model of Kuka’s youBot Mobile Manipulator. The mathematical model is developed using MATLAB software and the virtual animation setup of the robot is developed using the Virtual Robot Experimentation Platform (V-REP) robot simulator. By actuating the Kuka’s youBot mobile manipulator in the V-REP tool, it is observed to produce a tracking accuracy of 92% for physical interaction and object handling tasks

Antioxidant defence of L-glutamine on mitochondrial function in experimentally induced myocardial infarction in rats

Myocardial infarction is a major public health concern and the leading cause of death all over the world. A better understanding of the processes involved in myocardial infarction has stimulated the search for biomolecules, which could limit the myocardial injury. We determined the protective activity of L-glutamine on mitochondrial function in isoprenaline-induced myocardial infarction in rats, an animal model of myocardial infarction in man. Oral pre-treatment with glutamine significantly inhibited the isoprenaline-induced changes in the levels of troponin T and homocysteine in the plasma. It conserved the activities of tricarboxylic acid cycle enzymes (isocitrate dehydrogenase, α-ketoglutarate dehydrogenase, succinate dehydrogenase, malate dehydrogenase) and respiratory marker enzyme (NADH dehydrogenase) and the level of myocardial ATP content at levels comparable to that of normal controls. It also attenuated isoprenaline-induced oxidative stress in rat mitochondria and preserved the antioxidant defence system at near normal. The results indicate that the cardioprotective effect of glutamine can be correlated directly with its ability to activate the energy status and antioxidant defence system.Keywords: isoprenalin

Protective effect of L-glutamine on lysosomal integrity in isopoterenol-induced myocardial infarction in rats

Lysosomal acid hydrolases play an imperative part in the initiation of inflammation processes associated with myocardial infarction. In the present investigation, we have studied the protective effect of L-glutamine on isoproterenol-induced myocardial damage in male albino rats with respect to alterations in lysosomal function. The myocardial infarction in experimental animals was induced by intraperitoneal (i.p.) injection of isoproterenol (11 mg/100 g body weight/day) for 2 days. The total and free activities of lysosomal acid hydrolases (β-glucuronidase, β-galactosidase, β-glucosidase and acid phosphatase) were determined in plasma and heart tissue of control and treated rats. Significant elevation in the total activities of lysosomal hydrolases was observed in plasma and heart tissue of isoproterenol administered rats. A parallel (p<0.05) rise in the free activities of these acid hydrolases in the cardiac tissue was also noticed. Isoproterenol-mediated lysosomal membrane fragility was evident from the altered subcellular distribution of heart β-glucuronidase activity. Prior oral administration of L-glutamine (100 mg/kg body weight/day for a period of 20 days) significantly attenuated the isoproterenol-induced release of these lysosomal hydrolases into the systemic circulation from the cardiac lysosomes and maintained the lysosomal stability at level comparable to that of control rats. The results of the present study suggested that the cardioprotective activity of glutamine might be related to its membrane-stabilizing property

MAP Kinase-Mediated Activation of RSK1 and MK2 Substrate Kinases

Mitogen-activated protein kinases (MAPKs) control essential eukaryotic signaling pathways. While much has been learned about MAPK activation, much less is known about substrate recruitment and specificity. MAPK substrates may be other kinases that are crucial to promote a further diversification of the signaling outcomes. Here, we used a variety of molecular and cellular tools to investigate the recruitment of two substrate kinases, RSK1 and MK2, to three MAPKs (ERK2, p38α, and ERK5). Unexpectedly, we identified that kinase heterodimers form structurally and functionally distinct complexes depending on the activation state of the MAPK. These may be incompatible with downstream signaling, but naturally they may also form structures that are compatible with the phosphorylation of the downstream kinase at the activation loop, or alternatively at other allosteric sites. Furthermore, we show that small-molecule inhibitors may affect the quaternary arrangement of kinase heterodimers and thus influence downstream signaling in a specific manner

A Novel Goal oriented Sampling Method for Improving the Convergence Rate of Sampling based Path Planning for Autonomous Mobile Robot Navigation

Autonomous Mobile Robots' performance relies on intelligent motion planning algorithms. In autonomous mobile robots, sampling-based path-planning algorithms are widely used. One of the efficient sampling-based path planning algorithms is the Rapidly Exploring Random Tree (RRT). However, the solution provided by RRT is suboptimal. An RRT extension known as RRT* is optimal, but it takes time to converge. To improve the RRT* slow convergence problem, a goal-oriented sampling-based RRT* algorithm known as GS-RRT* is proposed in this paper. The focus of the proposed research work is to reduce unwanted sample exploration and solve the slow convergence problem of RRT* by taking more samples in the vicinity of the goal region. The proposed research work is validated in three different environments with a map size of 384*384 and compared to the existing algorithms: RRT, Goal-directed RRT(G-RRT), RRT*, and Informed-RRT*. The proposed research work is compared with existing algorithms using four metrics: path length, time to find the solution, the number of nodes visited, and the convergence rate. The validation is done in the Gazebo Simulation and on a TurtleBot3 mobile robot using the Robotics Operating System (ROS). The numerical findings show that the proposed research work improves the convergence rate by an average of 33% over RRT* and 27% over Informed RRT*, and the node exploration is 26% better than RRT* and 20% better than Informed RRT*

The mode of action of the Protein tyrosine phosphatase 1B inhibitor Ertiprotafib

Protein tyrosine phosphatase 1B (PTP1B) is a validated therapeutic target for the treatment of diabetes and obesity. Ertiprotafib is a PTP1B inhibitor that reached the clinical trial stage for the treatment of diabetes. Interestingly, Ertiprotafib reduces the melting temperature of PTP1B in differential scanning fluorimetry (DSF) assays, different from most drugs that increase the stability of their target upon binding. No molecular data on how Ertiprotafib functions has been published. Thus, to gain molecular insights into the mode of action of Ertiprotafib, we used biomolecular NMR spectroscopy to characterize the molecular details of the PTP1B:Ertiprotafib interaction. Our results show that Ertiprotafib induces aggregation of PTP1B in a concentration dependent manner. This shows that the insufficient clinical efficacy and adverse effects caused by Ertiprotafib is due to its tendency to cause aggregation of PTP1B.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

Abstract In smart cities, ensuring road safety and optimizing transportation efficiency heavily relies on streamlined road condition monitoring. The application of Artificial Intelligence (AI) has notably enhanced the capability to detect road surfaces effectively. This study presents a novel approach to road condition monitoring in smart cities through the development of an acoustic data processing and analysis module. It focuses on four types of road conditions: smooth, slippery, grassy, and rough roads. To assess road conditions, a microphone integrated road surface detector unit is designed to collect audio signals, and an ultrasonic module is used to observe the road depth information. The whole hardware unit is installed in the wheel rim of the vehicles. The data collected from the road surfaces are then analyzed using machine learning algorithms, such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrate the effectiveness of the proposed method in accurately identifying different road conditions. From these results, it was observed that the MLP provides better accuracy of 98.98% in assessing road conditions. The study provides valuable insights into the development of a more efficient and reliable road condition monitoring system for delivering secure transportation services in smart cities