The paper addresses the warehouseman's problem with geometrical simplifications under the more realistic case of imperfect 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. Thus, we need to approximate the state information - taking the highly nonlinear nature of the resulting system into account. This is accomplished using particle filters - which implement 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 estimate. Enhancements in the robot's movements and reduction in the number of collisions have been verified through extensive computer simulations.