Robot parts' rearrangement-sensor uncertainty reduction using particle filters

Abstract

This thesis adresses the parts’ moving problem under noisy sensory information. In this scenario, a 2D workspace contains an actuated robot and a set of unactuated parts. The discrepancy between the robot’s and/or the parts’ real and measured positions may lead to jerky movements or even collisions in the parts’ moving problem we are concerned with. In contrast to previous work, sensory data is no longer assumed to be perfect. Hence the robot needs to approximate state information, taking its highly nonlinear nature into account. It accomplishes this using particle filters, which implement a recursive Bayesian filter in nonlinear and/or nongaussian environments. For the model of parts which turns out to be linear, the approach reduces to Kalman filtering. First the robot’s dynamic model and the measurement model are modified to incorporate the inaccuracies in the sensory data; and then the particle filter is utilized to get improved positional estimates. Enhancements in the robot’s movements and reduction in the number of collisions have been verified through extensive computer simulations. An evaluation of its theoretical performance is presented based on the Cramer-Rao lower bound. Finally, a series of experiments with EDAR provide insight into real-time performance.