Modeling Induced Master Motion in Force-Reflecting Teleoperation

Providing the user with high-fidelity force feedback has persistently challenged the field of telerobotics. Interaction forces measured at the remote site and displayed to the user cause unintended master device motion. This movement is interpreted as a command for the slave robot and can drive the closed-loop system unstable. This paper builds on a recently proposed approach for achieving stable, high-gain force reflection via cancellation of the master mechanism’s induced motion. Such a strategy hinges on obtaining a good model of the master’s response to force feedback. Herein, we present a thorough modeling approach based on successive isolation of system components, demonstrated on a one-degree-of-freedom testbed. A sixth-order mechanical model, including viscous and Coulomb friction as well as a new method for modeling hysteretic stiffness, describes the testbed’s high-frequency resonant modes. This modeling method’s ability to predict induced master motion should lead to significant improvements in force-reflecting teleoperation via the cancellation approac

Robust adaptive synchronisation of a single-master multi-slave teleoperation system over delayed communication

Considering communication delays in networked multi-robot teleoperation systems, this paper proposes a new control strategy for synchronisation and stability purposes. A single-master and multi-slave (SMMS) networked robotic teleoperation system is considered. Based on a sliding surface combined with a smooth filtering and estimation methodology, a robust adaptive control is developed to guarantee the synchronisation and stability of the system in the presence of network-induced time-varying delays. Extensive simulation studies demonstrate the effectiveness of the developed control scheme

An Analysis of Sampling Effect on the Absolute Stability of Discrete-time Bilateral Teleoperation Systems

Absolute stability of discrete-time teleoperation systems can be jeopardized by choosing inappropriate sampling time architecture. A modified structure is presented for the bilateral teleoperation system including continuous-time slave robot, master robot, human operator, and the environment with sampled-data PD-like + dissipation controllers which make the system absolute stable in the presence of the time delay and sampling rates in the communication network. The output position and force signals are quantized with uniform sampling periods. Input-delay approach is used in this paper to convert the sampled-data system to a continuous-time counterpart. The main contribution of this paper is calculating a lower bound on the maximum sampling period as a stability condition. Also, the presented method imposes upper bounds on the damping of robots and notifies the sampling time importance on the transparency and stability of the system. Both simulation and experimental results are performed to show the validity of the proposed conditions and verify the effectiveness of the sampling scheme

Neural network enhanced robot tool identification and calibration for bilateral teleoperation

© 2013 IEEE. In teleoperated surgery, the transmission of force feedback from the remote environment to the surgeon at the local site requires the availability of reliable force information in the system. In general, a force sensor is mounted between the slave end-effector and the tool for measuring the interaction forces generated at the remote sites. Such as the acquired force value includes not only the interaction force but also the tool gravity. This paper presents a neural network (NN) enhanced robot tool identification and calibration for bilateral teleoperation. The goal of this experimental study is to implement and validate two different techniques for tool gravity identification using Curve Fitting (CF) and Artificial Neural Networks (ANNs), separately. After tool identification, calibration of multi-axis force sensor based on Singular Value Decomposition (SVD) approach is introduced for alignment of the forces acquired from the force sensor and acquired from the robot. Finally, a bilateral teleoperation experiment is demonstrated using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrated that the calibration of the force sensor after identifying tool gravity component by using ANN shows promising performance than using CF. Additionally, the transparency of the system was demonstrated using the force and position tracking between the master and slave manipulators