Neural-Network-Based Contact Force Observers for Haptic Applications

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