An efficient algorithm for generating an optimal plan for part-bringing tasks, using robotic manipulators, is introduced. The task of transporting a micro-part in a partially unstructured environment, that includes obstacles whose locations are not initially known, is introduced with the optimal plan formulated on the basis of the observed environmental conditions. Fuzzy set theory, well-suited to the management of uncertainty, is introduced to address the uncertainty associated with the part-bringing procedure. A part-bringing algorithm for generating the optimal plan related to a part assembly, despite existing obstacles, is presented. It is shown that the machine organizer using a sensor system can intelligently determine an optimal plan, based on explicit performance criteria, to overcome environmental uncertainty. The algorithm utilizes knowledge processing functions such as machine reasoning, planning, memory, and decision-making. Simulation results show the effectiveness of the proposed approach. The proposed algorithm is applicable not only to a wide range of robotic tasks including pick and place operations and maneuvering mobile based robots around obstacles, but also to the control of unmanned aircraft.