In our previous work [1], the potential benefits of Magneto-Rheological Fluid based actuators to the field of haptics were studied. Our results showed that the superior mechanical attributes of such actuators contribute to improvement of stability and transparency in haptic devices. To this end, a novel design of a small-scale MRF-based clutch, was proposed in [1]. This paper reports on the development and validation of the proposed MRF-based clutch. In addition, a closed-loop torque control strategy is presented. The feedback signal used in this control scheme comes from the magnetic field measurement and is used to compensate for the nonlinear behavior using an estimated model, based on Artificial Neural Networks (ANNs). Such a control strategy eliminates the need for torque sensors for providing feedback signals. The performance of the developed design and the effectiveness of the proposed modeling and control techniques are experimentally validated. The results clearly demonstrate that the clutch shows great potential for use in a multiple degrees-of-freedom (DOF) haptic interface for a class of medical applications.

This paper reports on the performance evaluation of Magneto-Rheological Fluid (MRF) based actuation systems when used in haptic interfaces. MRF-based actuators exhibit superior characteristics, which can significantly contribute to the transparency and stability of haptic devices. To validate this statement, a prototype two degrees-of-freedom (DoF) haptic interface is constructed. A distributed antagonistic configuration is used in order to develop the 2-DoF haptic interface based on small-scale MRF-based clutches for a class of medical applications. The developed device is compared with three conventional haptic interfaces and their stability is compared using the Virtual Wall experiment. Next, the prototype interface is incorporated in a master-slave teleoperation medical setup. Preliminary studies on the performance of the haptic interface show great potential of MRF-based actuators for integration in haptic devices for medical interventions that require safe, accurate, and stable force representation.

The two main objectives in designing a haptic interface are stability and transparency. The dynamics of the actuators employed in a haptic interface have a significant effect on these goals. In this article, the potential benefits of Magneto-Rheological Fluid (MRF) based actuators to the field of haptics are discussed. Devices developed with such fluids are known to possess superior mechanical characteristics over conventional servo systems. This contributes significantly to improved stability and transparency of haptic devices. In this study, this idea is evaluated from both theoretical and experimental points of view. First, the properties of such actuators which motivated this research are discussed. Next, two single degrees-of-freedom (DOF) haptic interfaces are used in a virtual wall experiment. These devices take advantage of an MRF-based clutch and a brushless DC motor at their core, respectively. The results of both devices are compared and show the superiority of the MRF-based clutch. In addition, design and analysis of a small-scale MRF-based clutch, suitable for a multi-DOF haptic interface, is given and its torque capacity, inertia, and mass are compared with those of conventional servo systems. Conclusions drawn from this investigation indicate that MRF clutch actuation approaches can indeed be developed to design haptic interfaces with improved stability and transparency.

Interactive robots are required to have minimal footprint on the shop floor and to be able to work in constrained areas while ensuring the safety of the humans. To this end, modeling of an unstructured environment including the humans is an indispensable part of the online control schemes deployed in such robots. In this regard, a new approach is proposed for generating an efficient model of the human body. This model takes advantage of superquadric functions to represent the human body more realistically than using primitive shapes, while offering minimal computational complexity and simplicity of further computations in comparison to the existing articulated models. This approach is also capable of incorporating various body postures and arbitrary arm configurations in the model. In addition, a new and intelligent sensory system, called floor mat, is introduced which can significantly contribute to generation of the proposed model, in a timely manner. The integration of the floor mat in a multi-sensory system for obtaining all required data for rendering the real-time 3D human model is then discussed. The use of superquadric-based human model in conjunction with the proposed sensing techniques provides an accurate, yet computationally efficient solution for safe human-robot interactions (HRI).

The integration of industrial robots into the human workspace presents a set of unique challenges. This paper introduces a new sensory system for modeling, tracking, and predicting human motions within a robot workspace. A reactive control scheme to modify a robot's operations for accommodating the presence of the human within the robot workspace is also presented. To this end, a special class of artificial neural networks, namely, self-organizing maps (SOMs), is employed for obtaining a superquadric-based model of the human. The SOM network receives information of the human's footprints from the sensory system and infers necessary data for rendering the human model. The model is then used in order to assess the danger of the robot operations based on the measured as well as predicted human motions. This is followed by the introduction of a new reactive control scheme that results in the least interferences between the human and robot operations. The approach enables the robot to foresee an upcoming danger and take preventive actions before the danger becomes imminent. Simulation and experimental results are presented in order to validate the effectiveness of the proposed method.

In this paper a reactive control strategy intended for human-robot interactions (HRI) is presented. A conventional reactive control scheme is reviewed first. This is followed by the introduction of a new prediction-based reactive control strategy. The new control strategy considers foreseeable dangerous events by predicting human motion using artificial neural networks, based on the previous pattern of the motion. This approach enables a robot to foresee an upcoming danger in order to take preventive actions before the danger is immanent. Experimental results for a CRS-F3 robot manipulator are presented in order to demonstrate and validate the effectiveness of this method.

In this paper, we have taken advantage of reinforcement learning to develop a new traffic shaper in order to obtain a reasonable utilization of bandwidth while preventing traffic overload in other part of the network and as a result, reducing total number of packet dropping in the whole network.. We used a modified version of Q-learning in which a combination of neural networks keeps the data of Q-table in order to make the operation faster while keeping the required storage as small as possible. This method shows satisfactory results in simulations from the aspects of keeping dropping probability low while injecting as many packets as possible into the network in order to utilize the free bandwidth as much as possible. On the other hand the results show that the system can perform in situations that are not originally designed to act in

In this paper, a new intelligent traffic shaper is proposed to obtain a reasonable utilization of bandwidth while preventing traffic overload in other part of the network and as a result, reducing total number of packet dropping in the whole network. This approach trains an intelligent agent to learn an appropriate value for token generation rate of a Token Bucket at various states of the network. This method shows satisfactory results in simulations from the aspects of keeping dropping probability low while injecting as many packets as possible into the network by minimization of used buffer size at each router in order to keep the delay occurred by packets waiting in long buffers to be sent, as small as possible