Sharif Digital Repository

Micro/Nano mechatronic systems might be defined as systems that include nano- or micro-scale components. These components can be sensors, actuators, and/or physical structures. Furthermore, the high-precision control laws for such small scales are important to ensure stability, accuracy, and precision in these systems. In this writing, four categories of such small-scale systems are considered by providing multifarious novel or key examples from the literature: control engineering and modeling, design and fabrication, measurement engineering, and sensor/actuator development. The applications discussed in the examples vary from nano-positioners, crucial in systems such as atomic force... 

In this paper, a new haptic interaction is presented where the operator is in contact with the haptic device (HD) only when she/he is in contact with the virtual environment (VE). This is in contrast with traditional haptic systems, where the operator is always in contact with the HD, even if she/he is out of the VE. In this haptic interaction, a visual tracking system is used to track the operator’s finger. When the finger is out of the VE, the HD tracks the finger so that the stylus of the HD keeps a constant distance of about 2 cm from the finger. When the finger gets close to the VE, the stylus slows down and stops upon reaching the VE; it then waits until the operator touches the stylus... 

We have introduced a new low-Reynolds-number microrobot with high 3D maneuverability. Our novel proposed microrobot has a higher rank of the controllability matrix with respect to the previous microswimmers which makes it capable of performing complex motions in space. In this study, governing equations of the microswimmer's motion have been derived and simulated. Subsequently, we have proposed a cascade optimal control technique to control the swimmer trajectory. In the proposed control scheme, the actuation is provided in a way that an exponential stability on the system trajectory error as well as minimum fluctuations of control signals are achieved. © The Author(s), 2021. Published by... 

Deep neural networks are currently the best solution for road and traffic scene interpretation for autonomous and self-driving vehicles. Compared to the autonomous cars, motorcycles have significant flexibility and advantages in crowded traffic situations and especially in non-urban and off-road areas. Many off-road tracks especially for agriculture and environment management tasks are only traversable with motorcycles. In this paper, a deep neural network is used for design and implementation of the vision system for navigation of an autonomous motorcycle. The proposed framework is evaluated using real world scenarios captured by a real motorcycle in various complex situations. The... 

Swimming microrobots have a variety of applications including drug delivery, sensing, and artificial fertilization. Their small size makes onboard actuation very hard, and therefore an external source such as the magnetic field is a practical way to steer and move the robot. In this paper, we have designed a novel microrobot steered by magnetic paddles. We have also discussed design parameters where, based on the conducted simulation, the robot speed reaches 520 um/s. It is shown that the microrobot speed depends on the robot paddle dimensions. According to the microrobots motion characteristics and their different reactions to the same input, we have designed a steering strategy for... 

This paper proposes a new design of robust control combining feedback linearization, backstepping, and sliding mode control called FLBS applied to the locomotion of five-link biped robot. Due to the underactuated robot's model, the system has a hybrid nature, while the FLBS control can provide a stabilized walking movement even with the existence of large disturbances and uncertainties by implementing smooth chatter-free signals. Stability of the method is proven using the Lyapunov theorem based on the hybrid zero dynamics and Poincaré map. The simulations show the controller performance such as robustness and chatter-free response in the presence of uncertainty and disturbance. Copyright ©... 

Dynamic modeling is a fundamental step in analyzing the movement of any mechanical system. Methods for dynamical modeling of constrained systems have been widely developed to improve the accuracy and minimize computational cost during simulations. The necessity to satisfy constraint equations as well as the equations of motion makes it more critical to use numerical techniques that are successful in decreasing the number of computational operations and numerical errors for complex dynamical systems. In this study, performance of a variant of Kane's method compared to six different techniques based on the Lagrange's equations is shown. To evaluate the performance of the mentioned methods,... 

Social robots may soon be able to play an important role in expanding communication with the deaf. Based on the literature, adaptive user interfaces lead to greater user acceptance and increased teaching efficiency compared to non-adaptive ones. In this paper, we build a robotic architecture able to simultaneously adjust a robot’s teaching parameters according to both the user’s past and present performance, adapt the content of the training, and then implement it on the RASA robot to teach sign language based on these parameters in a manner similar to a human teacher. To do this, a word to teach in sign language, repetition, speed, and emotional valence were chosen to be adaptive using a... 

Sign language is a non-verbal communication tool used by the deaf. A robust sign language recognition framework is needed to develop Human–Robot Interaction (HRI) platforms that are able to interact with humans via sign language. Iranian sign language (ISL) is composed of both static postures and dynamic gestures of the hand and fingers. In this paper, we present a robust framework using a Deep Neural Network (DNN) to recognize dynamic ISL gestures captured by motion capture gloves in Real-Time. To this end, first, a dataset of fifteen ISL classes was collected in time series; then, this dataset was virtually augmented and pre-processed using the “state-image” method to produce a unique... 

In this study, we introduce a novel three-dimensional micro-scale robot capable of swimming in low Reynolds number. The proposed robot consists of three rotating disks linked via three perpendicular adjustable rods, actuated by three rotary and three linear motors, respectively. The robot mechanism has an important property which makes it superior to the previously designed micro swimmers. In fact, our goal is designing a micro swimmer which its controllability matrix has full rank and hence it will be capable to perform any desired maneuver in space. After introducing the mechanism and derivation of the dynamical equations of motion, we propose a control method to steer the robot to the... 

This paper addresses the lack of proper Learning from Demonstration (LfD) architectures for Sign Language-based Human–Robot Interactions to make them more extensible. The paper proposes and implements a Learning from Demonstration structure for teaching new Iranian Sign Language signs to a teacher assistant social robot, RASA. This LfD architecture utilizes one-shot learning techniques and Convolutional Neural Network to learn to recognize and imitate a sign after seeing its demonstration (using a data glove) just once. Despite using a small, low diversity data set (~ 500 signs in 16 categories), the recognition module reached a promising 4-way accuracy of 70% on the test data and showed... 

Steel silos are one of the main structures for bulk solids handling and storage in many industries and agricultural sectors. A growing body of research suggests the importance of including the vertical component of earthquake ground motion in seismic analysis and design of certain structures. In the case of steel silos, this may induce additional meridional compression to exacerbate buckling failures in such thin shells. Accordingly, this paper investigates the buckling behavior of three cylindrical steel silos (i.e., a squat, an intermediate slender and a slender silo) with stepped walls subjected to horizontal only (H) and horizontal and vertical (HV) ground accelerations to address this... 

Semantic segmentation is one of the basic, yet essential scene understanding tasks for an autonomous agent. The recent developments in supervised machine learning and neural networks have enjoyed great success in enhancing the performance of the state-of-the-art techniques for this task. However, their superior performance is highly reliant on the availability of a large-scale annotated dataset. In this letter, we propose a novel fully unsupervised semantic segmentation method, the so-called Information Maximization and Adversarial Regularization Segmentation (InMARS). Inspired by human perception which parses a scene into perceptual groups, rather than analyzing each pixel individually, our... 

A major challenge in automatic irrigation of extensive agricultural fields is large scale soil moisture monitoring. Proper orthogonal decomposition (POD) is a widespread data-driven dimension reduction technique which can be combined with QR pivoting method for estimation of high-dimensional signals and optimal sensor placement. However, it requires computation of tailored basis functions which should be extracted from known training data. This is feasible for problems with constant features such as face recognition. However, using fixed bases (and probably fixed sensor selection) may not be an appropriate approach for estimation of signals with time-variant features. This paper demonstrates... 

Cloud Manufacturing (CMfg), a new manufacturing paradigm, enables an opportunity for manufacturing enterprises to respond to a wide variety of demands. The CMfg concept integrates the distributed resources and capacities in a shared economy, transfers them to the virtual manufacturing services with various quality of service (QoS) properties. These services are invoked to collaborate in producing customized demands with specific functional and non-functional properties. The operational perspective of the service-demand matching problem is considered more in the literature; however, it is a challenging problem in the CMfg ecosystem. This paper evaluates the service-demand matching problem... 

Deficit in imitation skills is one of the core symptoms of children with Autism Spectrum Disorder (ASD). In this study, we have tried to look closer at the body gesture imitation performance of 20 participants with autism, i.e. ASD group, and 20 typically developing subjects, i.e. TD group, in a set of robot-child and human-child gross imitation tasks. The results of manual scoring by two specialists indicated that while the TD group showed a significantly better imitation performance than the ASD group during the tasks, both ASD and TD groups performed better in the human-child mode than the robot-child mode in our experimental setup. Next, to introduce an automated imitation assessment... 

An optical apta-nanosensor was designed and developed based on reduced graphene quantum dots (rGQDs) and multi-walled carbon nanotubes (MWCNTs) and applied for the selective detection of diazinon as one of the most widely used organophosphorus pesticides. Considering the GQDs and rGQDs high fluorescence emission and optical stability, efficient optical transducers could be designed and precise detection methods could be developed based on such transducers. Herein, by using rGQDs, diazinon specific aptamer, and MWCNTs, a simple economical fluorescence method has been introduced to detect and measure diazinon with the detection limit of 0.4 nM (0.1 μg/L) in the range of 4-31 nM, meeting the... 

Swarm motion is an amazing collective behavior in nature for energy saving. Inspiring this natural phenomenon in microorganisms’ swimming, we have proposed a motion strategy for a swarm of microrobots to reduce their energy consumption during path tracking. The investigated microrobot is an artificial Self-Propelled Microswimmer (SPM) with high maneuverability at low Reynolds number flow (Re ≪ 1). In this study, we have demonstrated that forming a swarm behavior with minimum energy consumption requires the microswimmers to be close enough to each other, since at small distances the hydrodynamic interactions of microswimmers reduce their energy consumption. Moreover, we also showed that... 

Swarm motion is an amazing collective behavior in nature for energy saving. Inspiring this natural phenomenon in microorganisms’ swimming, we have proposed a motion strategy for a swarm of microrobots to reduce their energy consumption during path tracking. The investigated microrobot is an artificial Self-Propelled Microswimmer (SPM) with high maneuverability at low Reynolds number flow (Re ≪ 1). In this study, we have demonstrated that forming a swarm behavior with minimum energy consumption requires the microswimmers to be close enough to each other, since at small distances the hydrodynamic interactions of microswimmers reduce their energy consumption. Moreover, we also showed that... 

Biofuels are important alternative fuel in recent years. In this work, the bioethanol supply chain planning is performed by BioEnergy Supply Model to optimally find the best pathways using economic and environmental objective functions in Iran. Then, different sustainability criteria focusing on environmental, economic, and social concerns are assessed for possible routes of the bioethanol production from available resources. Although agriculture residues and wastes, decrease the greenhouse gas emissions but municipal solid wastes are more economic and require less water. The agricultural wastes and residues improve the sustainability. Then, criteria focusing on environmental, economic, and...