# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Deep Learning Methods  
This subsection presents models that can be considered deep in the context of machine learning, using different types of neural networks, primarily recurrent networks. [PERSON] et al. [59] proposed a new, efficient approach to the safe control of warehouse robots. DRL algorithms allow the calculation of suboptimal solutions that may be unsafe. The given solutions are then transformed into a standard supervisory control problem with an automaton and a set of unsafe states. Using Supervisory Control Theory, also known as the Ramadge-Wonham Framework, constraints on plant behaviour are automatically created so that as many preset specifications as possible are met. The supervisor can stop the plant from generating a subset of controllable events, but there is no way to force it to generate an event. The solution provided was validated on simulation data. The innovative aspect of this work lies in the application of mathematical apparatus, specifically the Ramadge-Wonham Framework, to ensure safety in warehouse robot control. This approach presents a novel way to address the issue of unsafe suboptimal solutions. However, the mathematical complexity of this method could pose a barrier to its practical application, particularly for individuals without a strong mathematical background. Future work could focus on developing more accessible, user-friendly interfaces or tools that can leverage this mathematical framework, thereby broadening its applicability. [PERSON] et al. [60], in their paper, proposed a new \"Dreaming Variational Autoencoder\" model to speed up detection of potential threats. The authors cite that often expert systems already exist in fully automated warehouses, but are not flexible enough \begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **References** \\ \hline Deep learning methods & [PERSON] et al. [59], [PERSON] et al. [60], [PERSON] et al. [61], [PERSON] and [PERSON] [62], [PERSON] et al. [63], [PERSON] et al. [64], [PERSON] et al. [65], [PERSON] et al. [66], [PERSON] et al. [67], [PERSON] et al. [68], [PERSON] et al. [69], [PERSON] et al. [70]. \\ \hline Probabilistic methods & [PERSON] et al. [71], [PERSON] et al. [72], [73], [74], [PERSON] et al. [75]. \\ \hline Methods for solving Traveling-Salesman Problem & [PERSON] et al. [76], [PERSON] and [PERSON] [77], [PERSON] et al. [78]. \\ \hline Algorithmic methods & [PERSON] et al. [79], [PERSON] [80], [PERSON] et al. [81], [PERSON] et al. [82], [PERSON] et al. [83], [PERSON] et al. [84], [PERSON] et al. [85], [PERSON] et al. [86], [PERSON] et al. [87]. \\ \hline Others & [PERSON] et al. [88], [PERSON] et al. [89], [PERSON] et al. [90], [PERSON] et al. [91], [PERSON] et al. [92], [PERSON] et al. [93], [PERSON] [94], [PERSON] et al. [95], [PERSON] et al. [96], [PERSON] et al. [97], [PERSON] et al. [98]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Categorization of articles by the method employed to work in dynamic environments. The model works in three stages: (1) The prediction model learns the dynamics of environmental changes (transitions between states). (2) Reinforcement learning without models uses the prediction model to safely sample the environment. (3) The model is implemented in the real world. This is an interesting solution that is based on a graph of transitions. The result of this model is represented as the number of erroneous positions in the storage map in the form of a 32 \(\times\) 32 image, and each erroneous transition is marked as an error with a value of 1. They validate the solution on various Atari games. The innovative aspect of this work is the introduction of the \"Dreaming Variational Autoencoder\" model, designed to expedite the detection of potential threats in dynamic environments. This model, which operates in three distinct stages and is based on a graph of transitions, offers a fresh perspective on addressing challenges in fully automated warehouses.