This paper presents the design and validation of a lightweight Magneto-Rheological (MR) clutch, called Hybrid Magneto-Rheological (HMR) clutch. The clutch utilizes a hybrid magnetization using an electromagnetic coil and a permanent magnet. The electromagnetic coil can adjust the magnetic field generated by the permanent magnet to a desired value, and fully control the transmitted torque. To achieve the maximum torque to mass ratio, the design of HMR clutch is formulated as a multi-objective optimization problem with three design objectives, namely the transmitted torque, the mass of the clutch, and the magnetic field strength within the clutch pack. A prototype of the HMR clutch is fabricated and its dynamic performance is experimentally validated. Experimental results clearly demonstrate the advantages of the HMR clutch in applications requiring fast and precise motion and torque control.

Conventional methods formulate the calibration problem between a robot hand and a camera, also known as hand-eye-calibration problem, as AX = XB. However, in practice, these methods have limited accuracy as will be shown in our experimental results. In this paper we formulated the hand-eye calibration problem as a point set matching problem and propose a new approach to solve this problem. The proposed approach is particularly suitable for robotic applications and offers good accuracy. We obtain a solution for the said problem using gradient descent (GD) technique on the Special Euclidean group SE(3). We call this approach GD-SE(3). To prove the validity of the proposed approach and to demonstrate its advantages, experimental results are provided where we compare the performance of GD-SE(3) with both conventional solutions for hand-eye calibration problem as well as those based on point set matching. The results show that the accuracy of GD-SE(3) is comparable with those based on other point-set matching algorithms. Yet, it outperforms conventional formulations based on AX = XB while offering a more suitable approach for the hand-eye calibration problem of robot manipulators.

This paper presents the design and evaluation of a new sensorized underactuated self-adaptive finger. The design incorporates a two-degrees-of-freedom (DOF) link-driven underactuated mechanism with an embedded load cell for contact force measurement and a trimmer potentiometer for acquiring joint variables. The utilization of proprioceptive (internal) sensors results in tactile-like sensations in the finger without compromising the size and complexity of the proposed design. To obtain an optimum finger design, the placement of the load cell is analyzed using Finite Element Method (FEM). The design of the finger features a particular rounded shape of the distal phalanx and specific size ratio between the phalanxes to enable both precision and power grasps. A quantitative evaluation of the grasp efficiency by constructing a grasp wrench space is provided. The effectiveness of the proposed design is verified through experimental results that demonstrate the grasp external wrench tolerance, shape adaptability, and tactile capability. All CAD files and ROS package for the proposed underactuated design can be found on https://github.com/mahyaret.

Bilateral telerobotic systems have attracted a great deal of interest during the last two decades. The major challenges in this field are the transparency and stability of remote force rendering, which are affected by network delays causing asynchrony between the actions and the corresponding reactions. In addition, the overactivation of stabilizers further degrades the fidelity of the rendered force field. In this article, a real-time frequency-based delay compensation approach is proposed to maximize transparency while reducing the activation of the stabilization layer. The algorithm uses a regulated bound-limited multiple Fourier linear combiner to extract the dominant frequency of force waves. The estimated weights are used in conjunction with the relatively phase-lead harmonic kernels to reconstruct the signal and generate a compensated wave to reduce the effect of the delay. The reconstructed force will then pass through a modulated time-domain passivity controller to guarantee the stability of the system. We will show that the proposed technique will reduce the force-tracking error by 40% and the activation of the stabilizer by 79%. It is shown, for the first time, that through the utilization of online adaptive frequency-based prediction, the asynchrony between transmitted waves through delayednetworks can be significantly mitigated while stability can be guaranteed with less activation of the stabilization layer.

In this letter, we present a novel inverse kinematics (IK) solution for a robotic arm-hand system to achieve precision grasp. This problem is kinematically over-constrained and to address the issue and to solve the problem, we propose a new approach with three key insights. First, we propose a human-inspired thumb-first strategy and consider one finger of the robotic hand as the “thumb” to narrow down the search space and increase the success rate of our algorithm. Second, we formulate the arm-thumb serial chain as a closed chain such that the entire arm-hand system is controlled as a hybrid parallel-serial system. The closed-chain formulation simplifies the task hierarchy of the entire arm-hand system. Third, we attach a virtual revolute joint to the thumb's tip with its rotation axis aligning with the thumb's contact normal The virtual joint will embody the thumb's functional redundancy. By selecting the thumb's joints including the added virtual revolute joint as active joints of the arm-thumb closed chain, the arm-thumb system's self-motion (i.e., the palm pose) and the thumb's functional redundancy can be directly controlled without using the null space projection. This provides a new possibility to control the self-motion of arm-hand systems. Simulation results will demonstrate the advantages and superior performance of the proposed approach for achieving precision grasp compared to other classical approaches.