In robot programming by demonstration (PBD) for assembly tasks, one of the important topics is to inference the poses and spatial relations of parts during the demonstration. In this paper, we propose a world model called assembly graph (AG) to achieve this task. The model is able to represent the poses of all parts, the relations, observations provided by vision techniques and prior knowledge in a unified probabilistic graph. Then the problem is stated as likelihood maximization estimation of pose parameters with the relations being the latent variables. Classification expectation maximization algorithm (CEM) is employed to solve the model. Besides, the contradiction between relations is incorporated as prior knowledge to better shape the posterior, thus guiding the algorithm find a more accurate solution. In experiments, both simulated and real world datasets are applied to evaluate the performance of our proposed method. The experimental results show that the AG gives better accuracy than the relations as deterministic variables (RDV) employed in some previous works due to the robustness and global consistency. Finally, the solution is implemented into a PBD system with ABB industrial robotic arm simulator as the execution stage, succeeding in real world captured assembly tasks.

Robot-centered cells play a more and more important role in the fields of incorporate automation and repetitive processing in order to increase productivity and to improve quality. In practice, it is always desired to achieve maximum/near-maximum throughput in a robotic cell. Sometimes, even a small improvement in throughput will be one of the highlighted objectives in robot scheduling, for instance, in the 3C industry of communication, computer and consumer electronics. In this paper, the scheduling problem in an n-workstation (n<;=8) m-stage robotic cell with parallel workstations is discussed by considering the constraints in real engineering practice, including free/non-free pick-up, allowed time window, and free/non-free process. A new method is proposed for optimal scheduling by means of optimally arranging each blocked cycles in every LCM cycle (Least Common Multiple), and assembling them in a proper order. This method is applied to a practical scheduling problem for a 7-workstation robotic cell. It is shown that the throughput is dramatically enhanced after optimization in the engineering scenario. Moreover, by comparing the resultant increase in revenue with the additional equipment costs, the result diagrams also provide managerial insights into links between throughput and cell layout flexibility.