# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Algorithm-Based Methods
Their research presents a careful consideration of the dynamics involved in the collision avoidance of these cranes. The authors proposed a simple algorithm for the movement of multiple stacker cranes. In short, an algorithm is used for collision avoidance in a crane movement system. It first chooses a trajectory from several candidates in \"I\" (a table containing trajectory information). If no trajectory can be chosen without collision, it attempts to adjust the trajectories to avoid collision. If collision still cannot be avoided, the crane transitions to a waiting state. If a deadlock situation occurs where two cranes have the same goal position and are both in the waiting state, the algorithm generates a trajectory to escape from the deadlock. The crane then moves according to the selected trajectory in \"I\". The algorithm aims to optimize travel time by delaying movement or making detours when necessary, but in experiments they verified, using simulations, the movement of two. Their method relied on the fact that in addition to avoiding collisions, the algorithm checked deadlocks and allowed stacker cranes to slow down and stop. In experiments, it transpired that the basic collision avoidance method was as good as their proposed method. Validation was performed on simulation data. The innovation of the aforementioned study stems from the introduction of an algorithm that, beyond merely avoiding collisions, factored in crane dynamics and checked for potential deadlock scenarios. Despite its innovative approach, the study found that the basic collision avoidance method performed on par with their intricate algorithm in simulated experiments. The findings also revealed that the proposed approach improved efficiency by 11% compared to simpler methods, with computation times remaining practical. Future endeavours might delve deeper into refining the algorithm or expanding its applications in diverse warehousing scenarios. In their innovative study, [PERSON] et al. [82] explore the use of GPS-enabled employee smartphones as a means of determining the location of forklifts within a warehouse. This approach contributes to enhancing safety measures within the warehouse environment. The trajectories were analyzed using what is known as a \"spaghetti chart\", a technique for drawing consecutive trajectories overlapping each other. However, this was only a simple preliminary analysis that did not yield interesting results. Nevertheless, this method was another that was tested in a real-world scenario. The authors introduced a pioneering technique by employing the \"smart spaghetti\" chart to depict consecutive overlapping trajectories, a methodology distinctively unique within the current literature. However, this approach is not without its limitations. It functions as a retrospective, offline qualitative analysis, devoid of real time monitoring and quantitative evaluations of reliability. While it is adept at pinpointing safety issues, the verification of suggested solutions remains challenging; it often requires supplementary methods such as logistic simulation. Looking ahead, there is potential to extend its use to varied warehouse sectors. Additionally, refining the model to forecast near misses in real-time based on worker movements is a promising avenue. Harmonizing the findings with predictive techniques or hands-on validation could further enhance its utility. The study conducted by [PERSON] et al. [PERSON] et al. (2018) presents an innovative MPC-based algorithm designed for Autonomous Unmanned Vehicles (UAVs). This algorithm is intended to predict the trajectories of neighbouring UAVs in situations where inter-UAV communication is not possible. The proposed algorithm is then integrated with the DMPC framework to realize trajectory planning of multiple UAVs in an environment with static obstacles. In the simulation of two scenes, they showed that the proposed method is feasible and effective. Moreover, this approach can attain performance similar to DMPC with communication, known as communication-free MPC.