Trajectory generation from noisy positions of object features for teaching robot paths

Llde, A., Trajectory generation from noisy positions of object features for teaching robot paths, Robotics and Autonomous Systems, 11 (1993) 113-127. In this paper we discuss a method for generating a trajectory describing robot path using a sequence of noisy positions of features belonging to a moving object obtained from a robot's sensor system. In order to accurately estimate this trajectory we show how uncertainties in the positions of object feature points can be converted into uncertainties in parameters describing the object pose (3-D position and orientation). Noisy estimations of the object poses, together with their uncertainties, are then used as an input to an algorithm that approximates the trajectory describing the robot path. The algorithm is based on natural vector splines and belongs to a family of non-parametric regression techniques which enable the estimation of the trajectory without requiring its functional form to be known. Since dilemma between specifying the trajectory either in Cartesian or in joint coordinates always exists, we present both alternatives. Some simulation results are given which illustrate the accuracy of the approach.