# Towards an Assembly Support System with Dynamic Bayesian Network
## 2 Related Work
The authors of [21] describe a human robot collaboration system that anticipates the parts needed by the operator and delivers them into three separate containers. The system predicts the points in time when the operator will need a part and will plan accordingly that the operator always has the three most likely needed parts available. The system is evaluated by the total assembly process execution time and the time the operator waits for the robot to deliver the needed parts. In [22], a variable length Markov model is used to predict the next operator actions using a temporal context recognized by a bi-stream convolutional neural network. The temporal context consists of the previous and current actions of the operator, without other additional features. Some drawbacks of [22] are the small number of experiment subjects and that the assembly is heavily restricted, with only two states in which the operator can choose his course of action. In contrast, in our work, we use DBN for assembly step prediction and our product has much more flexibility, allowing freedom of choice at any of the assembly states. DBNs have major applications in different fields: from gene sequence modeling in [23; 24] and crash prediction based on traffic speed in [25] to exchange rate predictability in [26]. In [27], the authors propose an approach for activity recognition based on DBN. They divided the features that describe the object motions in two classes: global and local, which are two different spatial scales. The global features describe the movement of the object at a large scale and the relations between the objects or the environment. Meanwhile, the local features represent the movement of the object of interest. The proposed DBN structure has a state duration that models the human interacting activities. Furthermore, the authors present the effectiveness of their approach with an experiment. The authors of [28] present an approach to measure network security using DBN. They stated that the security metrics measured individual vulnerabilities without consideration towards their combined effect. They propose a DBN model that aims to incorporate the temporal factors, such as the availability of patches or exploit codes. Potential applications of the DBN-based assembly prediction method are analyzed. Furthermore, they present how the DBN can be obtained from attack graphs and how it can be used to analyze the networks' security aspects. In our model, the time is incorporated through the sequentiality of the assembly steps. In [29], the authors use DBN for web search ranking. They state that the page position affects the number of times the page is clicked: the lower the position of the page, the less likely the user is to click on that page. This is called position bias. They propose a DBNthat aims to make an unbiased estimation of the relevance based on click logs. Their model has outperformed other click models in both clickthrough rate and relevance. In our previous works, we evaluated different next assembly step prediction methods. In [30], we analyzed two-level context-based predictors, which use the assembly context built up in the first level to select the corresponding pattern of the prediction table from the second level, whose associated next state will be the predicted one. The Markov predictor, presented in [31], improves the two-level predictor by storing for each pattern, beside the next states, the frequency of their apparition, thus predicting the state with the highest frequency. The Markov predictor was enhanced in [11] with a padding mechanism. In [12], we applied a prediction by partial matching algorithm that internally uses the Markov predictor presented in [11]. Furthermore, in [32], we implemented a long short-term memory recurrent neural network for the prediction of next assembly steps. The results of the prediction methods mentioned above will be presented in Section 4 comparatively with the results of the proposed DBN-based predictor. The DBN proved to be the most accurate model.