# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 4 Methods Classification
tory settings can still be considered reasonably representative of real-world applications, as some of the laboratories provided a close approximation of actual warehouse environments. When quantifying scientific papers, information on the availability of source code is also a crucial element. The ratio of publications making the source code available is shown in Figure 4 in the form of a pie chart. Analysis of the graph indicates that most accepted papers do not share the source code of their solutions. However, in the case of algorithm-based approaches, pseudo-codes of solutions are often posted. Most of the works that make their source code available are overwhelmingly those in the deep methods category. The \"Partial\" category has been introduced when authors have given another model as a base model and have made their own modifications to it, making it impossible to classify their work as \"unavailable\". In the final analysis, works in other categories were considered to lack information about the availability of the implementation. Following the analysis of the availability of source code in scientific papers, it becomes imperative to delve deeper into the specifics of the methods and algorithms employed. This leads us to our first research query. _RQ1. What Are the State-of-the-Art Methods and Algorithms Utilized in the Prediction of Object Trajectories within the Realm of Robotics and Automation?_  
With the aim of answering the first research question, the set of 39 identified papers was divided into five categories, namely:  
* Deep Learning methods;
* Probabilistic methods;
* Methods for solving the Traveling-Salesman Problem;
* Algorithmic methods and framework systems;
* Others. As demonstrated in Table 2, different methods have been employed in the field of motion trajectory prediction in. Currently, deep learning methods are considered state-of-the-art due to their effectiveness in checking ADE and FDE metrics and the availability of implementations. Nevertheless, their main disadvantage is that they are black-box solutions, so it is not possible to understand why a particular model chose a particular trajectory. Probabilistic methods are able to solve this problem, allowing determination of the probability of movement in different directions, but most of them do not have implementations, which hinders the possibility of their application. Methods based on the TSP problem were included in the review because of already existing systems that solve the problem of finding the optimal picking route in a warehouse, which can be a valuable addition to research that considers a dynamic environment. Figure 4: Pie chart showing the availability of implementation of accepted publications. Algorithmic methods are good reference points, as they present how safety management based on the location of objects in a warehouse has been handled in the literature, as well as what solutions are not advisable or desirable to achieve the desired results. Last but not least, the remaining body of literature, classified under the term others, yields valuable conclusions that could prove beneficial in the practical implementation of a location-based safety system in real-world settings.