In the presence of communication delays, master- slave teleoperation systems suffer from poor contact stability and sluggish performance. In this paper, a new impedance reflecting force-position (F-P) control architecture is proposed in which the master is in interaction with a local model of environment, thus bypassing the delayed contact force received at the master in a typical F-P controller. The local environment model is updated by the environment model parameters that are identified online at the slave and are transmitted to the master. However, due to the delay in model parameter transmission and the slow transition of the identified parameters at contact, the controller experiences contact oscillations in practice. As a remedy, a laser proximity sensor is employed to predict the collision time between the slave and the environment "a delay ahead of time". The incorporation of predicted contact time not only synchronizes the required sudden change in the environment local model dynamics with the remote environment contact event, but also allows for an increase in parameter identification convergence rate. The prediction method is introduced for slave unrestricted motion; however, it is verified on a single degree-of-freedom experimental setup. The performance of the proposed novel impedance reflecting F-P controller with collision prediction is compared to that of a typical F-P controller.

In the majority of robotic and haptic applications, including manipulation and human-robot interaction, contact force needs to be monitored and controlled. Transparent implementation of bilateral teleoperation or haptic controllers necessitates the exchange of operator and environment contact forces. This requires the use of expensive commercially available force/torque sensors, which are rather bulky, are vulnerable to impact forces, and increase system inertia and compliance. An alternative solution is the use of dynamic force observers, which estimate external forces using system dynamic model. However, due to the uncertainties in system dynamic structure and parameters, these model-based observers do not produce accurate force estimates, and often create a dynamic lag that may cause bandwidth limitation and instability. This paper proposes two neural-network-based force/torque observers that do not require a system dynamic model. The observers can estimate human hand force and environment contact force with up to 98.3% accuracy in the sense of mean-square error, and with negligible dynamic lag. The performance of the proposed observers are extensively analyzed in separate human-robot and robot-environment experimental settings, and in a two-channel bilateral teleoperation control loop with multiple runs with two Planar Twin-Pantograph haptic devices

In many studies and applications that include direct human involvement such as human-robot interaction, control of prosthetic arms, and human factor studies, hand force is needed for monitoring or control purposes. The use of inexpensive and easily portable active electromyogram (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non model-based estimation methods, “Multilayer Perceptron” Artificial Neural Networks (MLPANN) have widely been used to estimate muscle force or joint torque of different anatomy of humans or animals. This paper investigates the use of Radial Basis Function (RBF) ANN and MLPANN for force estimation and compares the performance of the two methodologies for the same human anatomy, i.e. hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper arm muscles involved in elbow joint movement and sensed elbow angular position and velocity are utilized as inputs to ANNs. Moreover, the use of elbow angular acceleration signal as input for ANN is investigated. Towards this end, a single degree-of-freedom robotic experimental testbed has been constructed, which allows for data collection, training and validation.

Transparent teleoperation under rate mode has proven to be difficult in terms of stability, performance and implementation. This is mainly due to the need for exchange of derivatives and integrals of measured positions and forces. This paper proposes and implements a new control architecture designed based on the environment impedance reflecting controller previously developed. The performance of this new controller, implemented on a haptic simulation test-bed, is compared to that of a conventional controller under different operational conditions.

Transparent teleoperation under rate mode has proven to be difficult in terms of stability, performance and implementation. This is mainly due to the need for the exchange of derivatives and integral of measured positions and forces. This paper proposes a new control architecture designed based on the environment impedance reflection concept. The performance of this controller is compared to that of a conventional controller, under different operational conditions using both analytical methods and numerical simulations.