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Human-robot collaborative (HRC) assembly combines the advantages of robot's operation consistency with human's cognitive ability and adaptivity, which provides an efficient and flexible way for complex assembly tasks. In the process of HRC assembly, the robot needs to understand the operator's intention accurately to assist the collaborative assembly tasks. At present, operator intention recognition considering context information such as assembly objects in a complex environment remains challenging. In this paper, we propose a human-object integrated approach for context-aware assembly intention recognition in the HRC, which integrates the recognition of assembly actions and assembly parts to improve the accuracy of the operator's intention recognition. Specifically, considering the real-time requirements of HRC assembly, spatial-temporal graph convolutional networks (ST-GCN) model based on skeleton features is utilized to recognize the assembly action to reduce unnecessary redundant information. Considering the disorder and occlusion of assembly parts, an improved YOLOX model is proposed to improve the focusing capability of network structure on the assembly parts that are difficult to recognize. Afterwards, taking decelerator assembly tasks as an example, a rule-based reasoning method that contains the recognition information of assembly actions and assembly parts is designed to recognize the current assembly intention. Finally, the feasibility and effectiveness of the proposed approach for recognizing human intentions are verified. The integration of assembly action recognition and assembly part recognition can facilitate the accurate operator's intention recognition in the complex and flexible HRC assembly environment. [ABSTRACT FROM AUTHOR]

Material requirements planning (MRP) is a kind of medium-term production planning, which aims to plan the end item requirements of the master production schedule over a finite planning horizon. In a smart factory, the customer requirements and the production status are varying in time, which increases the uncertainties in making lot-sizing decisions for MRP. In this study, a hybrid chance-constrained programming (HCCP) model is developed for solving an MRP problem with hybrid uncertainties, in which both randomness and fuzziness exist in a lot-sizing decision process. The objective of the HCCP model is to determine the lot sizes of all items while satisfying the stochastic demands and the fuzzy capacity constraints. The credibility and probability are incorporated into the proposed model to measure the fuzziness and randomness, respectively. In order to solve the model, relevant approaches for converting the probability-based and credibility-based constraints into the equivalent deterministic forms are proposed. Decision makers can set different confidence levels according to their own risk preferences to get different results. Finally, an example is presented to verify that the approach proposed in this paper is feasible for solving MRP problems with hybrid uncertainties. [ABSTRACT FROM AUTHOR]