Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanical Intelligence for Prehensile In-Hand Manipulation of Spatial Trajectories

The application of mechanical and other physical properties to the development of robotic systems that can easily adapt to changing external situations is known as mechanical intelligence. Following this concept, many robot hand designs can produce self-adaptive and versatile grasps with simple underactuated fingers and open-loop control, while mechanical- intelligent strategies for dexterous manipulation are still limited. This paper proposes a mechanical-intelligent technique to facilitate dexterous manipulation, in particular prehensile inhand manipulation. The proposed strategy is based on the generation of complex spatial trajectories of the hand-object system, controlled in open loop with the minimum number of actuators and using simple low-level non-position modes. This approach is exemplified by the rigorous analysis and testing of a three-fingered two-actuator underactuated robot hand, called the helical hand, which is capable of generating helical prehensile in-hand manipulation of diversiform objects under error tolerance controlled by constant speed algorithm.
Practical Time-Varying Formation Tracking Control for Multi-Agent Systems

This paper introduces the concept of formation switching in time-varying formation tracking (TVFT) control for linear time-invariant (LTI) multi-agent systems (MASs) under the collision avoidance constraints. One of the main challenges in designing a novel formation controller is that the leader agent does not prespecify the desired formation to the follower agents. Also, the former may change the desired formation to any other at the run time according to task requirements and environmental spatial constraints. Based on estimation of the so-called generator matrix and the formation vector, follower agents maintain the time-varying formation while tracking the leader. The uniform ultimate boundedness stability of the overall proposed distributed control scheme is proved using the Lyapunov stability theory. Finally, the effectiveness of the formation switching in TVFT is demonstrated using a numerical example.
Recent Developments in Plastic Optical Fiber based Wearable Sensors

Wearable sensors are a convenient method for monitoring human health parameters. In medical application, a number of wearable sensors have been developed to monitor different parameters like body temperature, muscle motion, heart rate, blood pressure etc. Plastic optical fiber (POF) is a suitable media for developing sensors for human parameters monitoring. The compact and light weight sensors developed by using plastic optic fiber has certain advantages compared to its counterparts. The immunity to electromagnetic interference makes fiber sensors suitable for medical instruments where other instruments are also operational in the surroundings. This paper reviews the recent developments in POF based wearable sensors.
FSG-Net: a Deep Learning model for Semantic Robot Grasping through Few-Shot Learning

Robot grasping has been widely studied in the last decade. Recently, Deep Learning made possible to achieve remarkable results in grasp pose estimation, using depth and RGB images. However, only few works consider the choice of the object to grasp. Moreover, they require a huge amount of data for generalizing to unseen object categories. For this reason, we introduce the Few-shot Semantic Grasping task where the objective is inferring a correct grasp given only five labelled images of a target unseen object. We propose a new deep learning architecture able to solve the aforementioned problem, leveraging on a Few-shot Semantic Segmentation module. We have evaluated the proposed model both in the Graspnet dataset and in a real scenario. In Graspnet, we achieve 40,95% accuracy in the Few-shot Semantic Grasping task, outperforming baseline approaches. In the real experiments, the results confirmed the generalization ability of the network.
Learning to Update Engine Models with Deep Reinforcement Learning

Model updating can reduce the reality gap between the digital space and physical space, which is essential for accurately building digital models of aviation equipment. However, with data requirements and the complexity of application environments continue to increase, traditional update techniques show great limitations. The main reason is that traditional update technologies are limited by computational efficiency, resulting in the inability to balance accuracy and real-time. In this work, we propose a model update method based on reinforcement learning to infer model parameters. The proposed method does not require any ground truth parameters. Experimental results on the twin-rotor turbofan engine model verify the superiority of the proposed method compared with other state-of-the-art methods.
Dual-band Asymmetric Bow-tie Double-sided Printed Dipole Antenna with Large Bandwidth at Both the Bands

In this communication, two dual-band Asymmetric Bow-Tie Double-Sided Printed Dipole Antennas (ABDSPDA) with large bandwidths at both bands are reported. Two triangular arms have different dimensions which resonates at two frequencies; operating in dual-band. The antennas are designed with two materials having Dielectric Constant (DK)=2.4 and 4.4. The ABDSPDA-1 designed with DK=2.4 provides the impedance bandwidth (S11 ≤ -10dB) of 23 % and 53% at the central frequencies 1.21 GHz and 3.36 GHz respectively; and with DK=4.4, it provides 25% and 26% at the frequencies 1.26 GHz and 3.24 GHz respectively. It is fed by the printed version of the two-wire transmission line. ABDSPDA-2 is also designed with the same DKs but the feed line attached to the ground radial is flared which acts as a Balanced to Unbalanced (BALUN) network; It improves impedance bandwidth in both bands. The ABDSPDA-2 designed with DK=2.4 provides impedance bandwidth (S11≤ -10dB) of 26% and 63% having central frequencies 1.21GHz and 3.87 GHz; And with DK=4.4, the bandwidth improves to 51 % and 27% having central frequencies 1.48 GHz and 3.35 GHz respectively. The antennas are fabricated with the available material of DK=4.4. The simulate and measured values are in good agreement.
Exploiting Intrinsic Kinematic Null Space for Supernumerary Robotic Limbs Control

Supernumerary robotic limbs (SRLs) gained increasing interest in the last years for their applicability as healthcare and assistive technologies. These devices can either support or augment human sensorimotor capabilities, allowing users to complete tasks that are more complex than those feasible for their natural limbs. However, for a successful coordination between natural and artificial limbs, intuitiveness of interaction and perception of autonomy are key enabling features, especially for people suffering from motor disorders and impairments. The development of suitable human-robot interfaces is thus fundamental to foster the adoption of SRLs. With this work, we describe how to control an extra degree of freedom by taking advantage of what we defined the Intrinsic Kinematic Null Space, i.e. the redundancy of the human kinematic chain involved in the ongoing task. Obtained results demonstrated that the proposed control strategy is effective for performing complex tasks with a supernumerary robotic finger, and that practice improves users' control ability.
Zero-Gradient-Sum Algorithm-Based Distributed Optimization in Finite Time for Agents on Signed Networks

Based on zero-gradient-sum (ZGS) algorithm, this paper studies the finite-time distributed optimization problem for multi-agent systems (MASs) over signed networks. First, a finite-time distributed control protocol is designed to ensure all agent states to realize bipartite consensus in a finite time under undirected signed graphs. Meanwhile, the optimized solutions of minimizing global convex objective functions under undirected signed graphs also converge to the bipartite consensus values. Moreover, the finite-time upper bound is given, which depends on the initial states and also the choice of the parameters in the proposed protocols. Then, extend the results to the case of signed digraphs. Finally, simulation results show the validity of the proposed methods.
Strained Elastic Surfaces with Adjustable-Modulus Edges (SESAMEs) for Soft Robotic Actuation

For robots to interact safely with humans and travel with minimal weight, low-density packable actuators are sought. Electronically-driven active materials like shape memory wire and other artificial muscle fibers offer solutions, but these materials need a restoring force. Moreover, if joint bending is required, the actuators must exert a bending moment around the joint. In this paper, we model the three-dimensional shapes of strained elastic surfaces with adjustable-modulus edges (SESAMEs), then implement SESAMEs by machine embroidering shape memory alloy wire onto stretched elastic fabric, showing a path to lightweight actuators that exert bending forces and have built-in restoring forces. SESAMEs start out planar, and upon release from the plane take on three-dimensional shapes thanks to the balance between bending energy in the boundary and strain energy in the elastic surface. The elastic creates both a restoring force to bring the boundary back to its original shape after actuation, and an out-of-plane structure for applying a bending moment. We demonstrate SESAMEs' properties as soft robotic actuators individually and in arrays, and coupled to flexible plastic frames during the planar fabrication process as bending actuators to switch bistable mechanical structures.
AlGaN/GaN HEMT based RF Switch Model

Indigenous AlGaN/GaN high electron-mobility transistor (HEMT) based RF switch model has been developed using measured data for various fabricated devices of different dimensions up to X band. Parameters of equivalent circuit of devices have been extracted using measured data. Switch model has been developed and verified in both ON and OFF state using measured data of different gate width varying from 200 µm to 1080 µm of devices. The devices have been fabricated using indigenous SSPL 0.25 µm GaN HEMT technology. This method consists of direct determination of both the extrinsic and intrinsic parameters. This model is very accurate, and it fits the S-parameters very well up to X band.
Fault Diagnosis Method of Aircraft Engine Based on ISO-BiLSTM

Aero-engine is an important component of aviation equipment. Due to the special working environment and the poor temporality of fault diagnosis, it is difficult for aircraft maintenance personnel to make accurate fault diagnosis of aero-engine. To address this problem, this paper proposes an aero-engine fault diagnosis method based on an improved snake optimization algorithm (ISO) and a bidirectional long and short-term memory network (BiLSTM). The idea of balanced pool of EO algorithm is introduced in the snake optimization algorithm to build an elite pool to compensate for the low accuracy of calculation caused by randomly selected individuals, while the Lévy flight perturbation mechanism is introduced to enhance the SO’s performance of finding the best; the optimized parameters are substituted into the BiLSTM to reconstruct the model, train the fault data for prediction and output the results. In the simulation experiments, five failure modes are diagnosed with real monitoring data of a certain type of aero-engine and compared with the BiLSTM and SO-BiLSTM fault diagnosis models. The experimental results show that the ISO-BiLSTM fault diagnosis method proposed in this paper has better fault diagnosis effect compared with BiLSTM and SO-BiLSTM, and provides a new idea for aero-engine fault diagnosis.
Adaptive output feedback control for nonlinear multi-agent systems with actuator failures

In this paper, the adaptive output feedback control is applied to investigate the global leader-follower consensus problem for a kind of nonlinear multi-agent systems subject to actuator failures. Firstly, introducing the first coordinate transformation to convert the unknown coefficient of the failures about the actuator into the unknown constant Lipschitz conditions of the multi-agent systems. Secondly, using the second coordinate transformation to make the consensus problem of the considered system into a stability problem. Immediately, the concemed system is turned into a system that is easy to find Lyapunov function after the third coordinate transformations. Finally, a series of adaptive output feedback controllers are constructed, which can address the problem of global leader-follower consensus and realize the ability to regulate the following error of each follower’s output signal. Additionally, the simplicity and effectiveness of the presented technique are verified by mathematical simulation results.
Health Disease Prediction Using Machine Learning and Internet of Things

The Analysis of Disease Forecast using Machine Learning may be a framework that makes an exact prediction based on the data or side effects that the user enters into the framework and the infection it will cause. If the understanding isn't very accurate and the client really needs to know the type of illness he or she has experienced, it could be a framework that offers the client the tips and tricks to maintain the client's wellbeing framework and it provides a way to discover the illness using this forecast. The health industry plays a crucial role in treating patients' infections, so there are typically a few types of offer assistance for the health industry. By just getting the client's indications and using the framework, the health business can also profit from it. They will be able to tell in a reasonable amount of time. With the help of machine learning and the Python programming language with Tkinter interface, this Malady Expectation Utilizing Machine Learning has been completed in full. It also uses the dataset that the medical facilities already have access to and uses that to predict the spread of infection. IoT, cloud, and fog computing, which are popular issues in the field of illness detection and prediction, are, on the other hand, attracting the attention of the academic community. The Internet of Things (IoT), cloud computing, and fog computing have all been used to develop a variety of healthcare solutions.
Asymmetric Dissipativity and Supply Rates for Compartmental Systems with Logarithmic Storage Functions

Many compartmental models in biology, chemistry, ecology, sociology, and related sciences have positive variables, for which equilibrium points of interest are in the interior of the positive orthant. Reactions are often modeled as a bilinearity, which renders a boundary of the positive orthant a separatrix. In such situations, the capability of Lyapunov functions defined on symmetric domains is very limited, and logarithmic functions are sometimes used in mathematical biology. The domain of a logarithm function is a ray, which is asymmetric with respect to the equilibrium of interest. This paper aims to add a useful tool to dissipativity theory for analyzing networks via storage functions defined on asymmetric spaces. This paper focuses on an asymmetric supply rate and discusses its properties and utilities which standard symmetric supply rates do not offer. The power of the asymmetric supply rate is illustrated briefly by establishing asymptotic stability of the endemic equilibrium of the SIQRS model of infectious diseases in an arbitrarily large domain.
Integral Torque Tracking with Anti-Windup Compensation and Adaptive Cadence Tracking for Powered FES-Cycling

Motorized functional electrical stimulation (FES) induced cycling is a rehabilitation intervention and exercise strategy that can benefit people with movement disorders. Power tracking is an objective in which leg muscles are artificially activated to track an active torque trajectory while an electric motor achieves a desired speed (cadence). Technical challenges remain to enhance muscle torque tracking performance since muscles experience saturation, which can induce error build-up. Further, the electric motor controller needs to account for the cross muscle torque input in the kinematic tracking loop and thus reduce potential cycling fluctuations. In this paper, a FES muscle torque tracking controller is designed with an anti-windup term in an integral torque error signal to mitigate the effect of muscle saturation. Then, an adaptive-based concurrent learning controller is developed for the electric motor to track cadence and estimate uncertain constant parameters of the cycle-rider system. The adaptive motor controller embeds the muscle torque (since the muscle controller is implementable) in the regressor and exploits it as a feedforward term in the cadence loop, rather than canceling the muscle torque input as it is usually performed in robust control designs. Globally uniformly ultimately bounded (GUUB) tracking is obtained for the torque tracking objective and exponential cadence tracking and parameter convergence is obtained by the learning controller after a finite excitation condition is satisfied.
H∞ Control of a Heated/Cooled Rod Under Point Actuation and Point Sensing

Our long term goal is to extend the H∞ paradigm to nonlinear, infinite dimensional systems under point actuation and point sensing. This paper is the first step in this direction. We find the minimum L2 gain from the noisy heat flux at one end of a rod to the temperature at an arbitrary point of the rod that can be achieved by state feedback on the heat flux at the other end of the rod. We do this by completing the square to obtain a Riccati partial differential equation whose solution yields a state feedback control law that achieves a given L2 gain. We solve the Riccati PDE by Fourier series. We iterate this process to find the minimum L2 gain.
A Hybrid Wearable Technology Model for Autism Behaviour Intervention: Components and Elements Analysis

Autism spectrum disorder is a neurodevelopmental condition that presents challenges in social interaction, communication, and restricted and repetitive behavior patterns. These behavior problems, including aggression, anxiety, and tantrums, can be disruptive for children with autism and their families, impacting their daily lives. Intervention strategies, such as wearable technology, can help to minimize these problems. To ensure effective intervention, it is essential to design devices that are specific to the preferences of children with autism, such as wrist-worn devices. Integrating persuasive, calm, and empathic technology design principles with the basic design of wearable technology for autism can result in a hybrid model that serves as a new guideline for developers in designing wrist-worn devices for children with autism. Through critical and comparative analysis of previous studies, relevant components and elements were extracted, elicited, and mapped to form a hybrid wearable technology model for autism behavior intervention. Ultimately, this model can help developers design effective wrist-worn devices for children with autism, minimizing their behavior impairments.
A mathematical model for the propagation of wildfires

In recent years, great attention has been focused by the scientific community on the study and implementation of monitoring systems in urban and suburban areas for fire prevention. Data from 2021 show that about 160 hectares of forest and non-forest areas in Italy have been destroyed by wildfires. For the purpose of this, the present conference paper aims to propose a preliminary mathematical model of reaction-diffusion to be used, with the data obtained from monitoring systems WNSs (Wireless Sensor Networks), for the purpose of predicting the development of a wildfire taking into account climatic and wind conditions, terrain topography and fuel present.
Nontangency-Based Lyapunov Tests for Convergence in Discrete-Time Dynamical Systems

This paper focuses on stability analysis of discrete-time dynamical systems having a continuum of equilibria. Two notions that are of particular relevance to such systems are convergence and semistability. In this paper, we develop Lyapunov-based tests for convergence using the notion of nontangency between the vector field and invariant subsets of the level and sublevel sets of the Lyapunov function and its difference. Specifically, we study nontangency of the vector field to the set of equilibria and provide sufficient conditions for convergence. Moreover, a relationship between Lyapunov stability of an equilibrium point and convergence is provided.
Fuzzy Logic-Based Approach for Location Identification and Routing in the Outdoor Environment

Finding the precise and accurate location of devices on road networks is challenging in remote areas with poor internet connectivity and Global Positioning System coverage. Navigation applications that completely depend on the internet and reference spatial data for location identification and mapping do not perform well in case of frequent internet disconnection. These reference spatial data sources have many associated challenges like large size, errors in data, and restricted access. To address these challenges, this paper provides an approach for localization and routing using self-generated reference data using likelihood estimation. According to the proposed approach, the trajectory information is used to create the reference data in the format of Comma Separated Values (CSV). This reference data is first analyzed for quality issues and then used for navigation purposes. Further for the localization Sugeno Fuzzy Model is used as a fuzzy inference system for the initial localization and subsequent mapping of the location. The proposed approach is validated using an Android application on seven predefined routes. According to the performed result analysis, the proposed fuzzy logic-based approach is able to provide location identification with 98.9 percent accuracy with a root mean square error value of 3 percent.
High-Confidence Trajectory Planning for Off-Road Automated Vehicles under Energy Constraints

For automated vehicles operating in off-road environments, there is substantial uncertainty in their energy needs and utilization. To account for this uncertainty, we propose a high-confidence global planner that obtains the path with the highest-confidence energy constraints are met. We outline a sampling-based method to approximate the energy stage cost uncertainty as a normal random variable, and then transform the uncertain optimal control problem to a deterministic one that can be solved using standard methods. We couple this with a local nominal model predictive controller that employs a dynamics model of the off-road vehicle on deformable terrains. We show through Monte-Carlo simulations that the framework is robust in the face of uncertainty in terms of energy consumption and outperforms approaches that simply plan for the minimum expected energy consumption.
Distributed fuzzy consensus control of second-order nonlinear systems with application to ship heading-keeping control

This note considers the distributed fuzzy consensus control of a kind of uncertain second-order nonlinear multiagent systems (MASs). By combining the fuzzy approximation approach with variable separation method, an adaptive distributed control scheme is proposed according to the relative local state message. The proposed adaptive distributed fuzzy protocol guarantees that the closed system is uniformly ultimte bounded, and consensus error can be constructed arbitrarily small. The effectiveness of this protocol is verified by applying it to the problem of ship heading-keeping control problem.