Neural network modelling of a compliant device to enhance and simplify compliant force control

A method is offered that uses an artificial neural network (ANN) to simplify the problem of mapping a robot tool position in space when using compliant force control. For this case, the compliant device (CD) is a moulded rubber block situated between the manipulator end flange and the tool. A force sensor is mounted between the tool and the CD. It is necessary to be able to relate the recorded contact forces and torques (hereafter referred to as “forces”) to the changes in translations and orientations (hereafter referred to as “positions”) of the tool relative to the manipulator. Usually, a complex Newtonian model would be required to achieve this. For this paper, to overcome the difficulties of accurately modelling the non-linear and highly coupled characteristics of the CD, back propagating ANNs have been trained to relate the forces to positions for real data sets. The finished ANN is to be used as part of a computer model of the overall manipulator system. This paper assesses the ability of the ANN to model the CD. In particular, three issues are addressed: first, the comparison of the ANN with a conventional spring type model; second, the verification of the ANN with true data sets; third, the need to filter the ANN output to avoid problems from outliers.