# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Algorithm-Based Methods  
This sub-section will present papers in which the authors propose solutions to the problem of predicting human trajectory in the context of security, not necessarily involving predicting that trajectory. In addition, this subsection will be assigned papers in which the authors present framework systems to support warehouse security. In their comprehensive study, [PERSON] et al. [79] delve into the potential of predicting warehouse workers' trajectories based on historical movement data. This is achieved through the employment of a momentum algorithm, offering valuable insights for applications within factory and warehouse environments. Although the authors do not provide implementation details, they prove the effectiveness of their solution by adding noise of \(+/-\) 10 cm and \(+/-\) 50 cm to the data. They note, however, that using too long motion history can lead to larger prediction errors. Authors validated their solution on simulation data. The authors' innovation was shown by exploration of predicting warehouse workers' movements using a momentum-based approach combined with ultra-wideband-based Real-Time Locating Systems (RTLS), aiming to enhance human-robot collaboration in production settings. Their methodology displayed strengths, particularly in collision prevention for Automated Guided Vehicles (AGVs). However, there are inherent limitations: over-reliance on extensive motion history can inflate prediction errors, and the study used simulation data without diving into granular implementation specifics. The momentum-based method's predictive scope remains confined, making it chiefly useful for short-horizon predictions. Looking ahead, there is a promising avenue in integrating worker destination predictions and considering floor plans to refine the trajectory predictions further. \begin{table}
\begin{tabular}{l l} \hline
**Evaluation Method** & **Papers** \\ \hline Simulation & [PERSON] et al. [76], [PERSON] and [PERSON] [77] \\ \hline Real-world case & [PERSON] et al. [78] \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of evaluation methods used in the papers on TSP-based methods. The master's thesis by [PERSON] [80] deals with the problem of organizing an autonomous warehouse. The author proposes a solution based on units moving autonomously, called Context-Aware Route Planning (CARP), which are controlled in an organized manner to achieve optimal routes. The CARP algorithm is based on A* and is used to solve the Multi-Agent Path Finding (MAPF) task. It finds a starting window for each agent to deploy them in the graph at a given time, and then performs an A* search with a cost function that considers the time at which vertices are visited. The algorithm optimizes for the makespan, or the time taken to complete the task, and uses heuristics to estimate the cost to the goal. It expands vertices by considering neighbouring vertices and their time windows to determine reachable windows for each agent. If the goal vertex is reached and is free forever, the algorithm finishes and reconstructs the found path. The work has been validated both on simulation and in a professional laboratory setting, where multiple robots have been deployed. The innovation presented in this work is the organization of an autonomous warehouse through the Context-Aware Route Planning (CARP) algorithm. It innovatively addresses the Multi-Agent Path Finding (MAPF) challenge by determining optimal starting windows and employing a unique cost function. However, certain heuristic assumptions, such as the precise estimate to the goal, did not outperform standard measures such as the Euclidean distance. Future research avenues include evaluating heuristic properties, enhancing memory utilization in MAPF solving, and possibly adapting existing algorithms such as D*Lite for the MAPF context. In their insightful study, [PERSON] et al. [81] tackle the challenge of overseeing the operation of autonomous stacker cranes within a warehouse environment.