# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Miscellaneous
However, a significant limitation surfaced during the study: the system, while efficient and user-friendly, overlooks essential security considerations. For future enhancements, a comprehensive security review and implementation are crucial to ensure the integrity and safety of the logistics data being managed. Next, [PERSON] et al. [90] present a proposal for analysing the use of mobile robots to transport and manipulate goods within a manufacturing workspace. The level of intelligence required for these tasks is determined by their complexity, adaptability, and ability to respond to dynamic interactions. The proposed system is based on a logical decision-making model and is implemented using a robotic warehouse model. Validation of this solution was based on miniature autonomous unmanned vehicles in the laboratory. The innovation presented in their work is a novel system for analysing the use of mobile robots in manufacturing workspaces to transport and manipulate goods. Their system is unique in that it is grounded in a logical decision-making model and is implemented using a robotic warehouse model. The study's distinctiveness is further underscored by its validation method, which employed miniature autonomous unmanned vehicles in a laboratory environment. However, the study highlighted several limitations. One of the primary concerns is the noise interference in the Real-Time Location System (RTLS), causing discrepancies in the exact positioning of forklifts--a critical issue given the precision required in narrow aisles of warehouses. Although they designed an algorithm to mitigate this, complete resolution of the RTLS noise remains a challenge and necessitates further refinement, potentially involving improved hardware technology. Another limitation was the absence of real-time data analysis, an essential component for Industry 4.0. For future work, there is a need for advancing RTLS technology to address noise and signal strength. The frameworks should be developed to synchronize warehouse data, creating real-time analysis systems equipped with cutting-edge machine learning methods, integrating RTLS for both items and forklifts tracking, and devising an automated decision support system embedded within an Industry 4.0 platform. In a related study, [PERSON] et al. [91] propose a novel method for order picking within a logistics setting. This method employs a combination of an HC strategy and a k-opt-based algorithm, demonstrating its potential within a multi-UAV system in an intelligent warehouse. They showed that their method outperformed other strategies and algorithms in terms of convergence time and lead time, even with an increase in order arrival rate. The proposed approach is suitable for logistics environments that use a lot of UAVs and require fast work. However, the authors acknowledged that some assumptions were made and further research is needed to account for UAV-to-UAV communication and different situations such as order removal and change. The approach was validated on a warehouse simulator. In conclusion, the authors' work provides a novel approach to improving order picking performance in logistics environments. The novelty of this research is based on the introduction of a new method. This dynamic path planning approach allows for immediate assignment of orders, offering a notable improvement in convergence time and lead time, particularly with rising order arrival rates. Despite its notable advancements, the study does have its constraints. The research assumes a fixed starting and ending point at a depot and does not account for potential UAV collisions. Furthermore, the centralized nature of the logistics allocation means there has to be a robust connection between all UAVs and the central network, which could pose challenges in practical applications. For future work, there are several avenues for enhancement and further exploration. Future studies will delve into facilitating order batching through inter-UAV communication to decentralize control.