Design of a Haptic Interface for Medical Applications using Magneto-Rheological Fluid based Actuators

This thesis reports on the design, construction, and evaluation of a prototype two degrees-of-freedom (DOF) haptic interface, which takes advantage of Magneto-Rheological Fluid (MRF) based clutches for actuation. Haptic information provides important cues in teleoperated systems and enables the user to feel the interaction with a remote or virtual environment during teleoperation. The two main objectives in designing a haptic interface are stability and transparency. Indeed, deficiencies in these factors in haptics-enabled telerobotic systems has the introduction of haptics in medical environments where safety and reliability are prime considerations. An actuator with poor dynamics, high inertia, large size, and heavy weight can significantly undermine the stability and transparency of a teleoperated system. In this work, the potential benefits of MRF-based actuators to the field of haptics in medical applications are studied. Devices developed with such fluids are known to possess superior mechanical characteristics over conventional servo systems. These characteristics significantly contribute to improved stability and transparency of haptic devices. This idea is evaluated and verified through both theoretical and experimental points of view. The design of a small-scale MRF-based clutch, suitable for a multi-DOF haptic interface, is discussed and its performance is compared with conventional servo systems. This design is developed into four prototype clutches. In addition, a closed-loop torque control strategy is presented. The feedback signal used in this control scheme comes from the magnetic field acquired from embedded Hall sensors in the clutch. The controller uses this feedback signal to compensate for the nonlinear behavior using an estimated model, based on Artificial Neural Networks. 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. Next, a 2-DOF haptic interface based on a distributed antagonistic configuration of MRF-based clutches is constructed for a class of medical applications. This device is incorporated in a master-slave teleoperation setup that is used for applications involving needle insertion and soft-tissue palpation. Phantom and in vitro animal tissue were used to assess the performance of the haptic interface. The results show a great potential of MRF-based actuators for integration in haptic devices for medical interventions that require reliable, safe, accurate, highly transparent, and stable force reflection

Design and performance evaluation of a prototype MRF-based haptic interface for medical applications

This paper describes the construction and stability and transparency evaluation of a prototype two degrees-of-freedom (DoF) haptic interface, which takes ad-vantage of magneto-rheological fluid (MRF)-based clutches for actuation. These small-scale clutches were designed in our lab, and their evaluation were reported previously [1],[2]. MRF-based actuators exhibit superior characteristics,which can significantly contribute to transparency and stability of haptic devices. Based on these actuators, a distributed antagonistic configuration is used to develop the2-DoF haptic interface. This device is incorporated in a master–slave teleoperation setup intended for medical per-cutaneous interventions and soft-tissue palpation. Preliminary studies on the stability and transparency of the haptic interface in this setup using phantom and ex vivo samples show the great potential of MRF-based actuators for integr-tion in haptic devices that require reliable, safe, accurate,highly transparent, and stable force reflection

A Streamlined Search Algorithm for Path Modifications of a Safe Robot Manipulator*

In recent years, the interest in human-robot interactions has added a new dimension to the on-line path planning problem by requiring a method that guarantees a risk-free path. This paper presents a streamlined search algorithm for fast path modification. The algorithm is formulated as an optimization problem that evaluates alternative paths nearby each obstacle. Each path is evaluated based on the value of the danger assigned to that path. To reduce the size of the search space, the minimum number of via points necessary to alter the path is initially obtained using a geometrical method. Given the number of via points, the algorithm proceeds to locate the via points around the obstacle such that the resulting path through these via points satisfies all problem constraints. Obtaining a solution in this way renders a fast algorithm for path modification, while it better avoids problems often encountered in other gradient-based search algorithms. Case studies for two planar robots are provided to highlight some of the advantages of the proposed algorithm. Experimental results using a CRS-F3 robot manipulator validate the effectiveness of the algorithm for applications involving human-robot interactions

Application of reinforcement learning in development of a new adaptive intelligent traffic shaper

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 i