# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Algorithm-Based Methods
It can achieve this with only a minor increase in computation time. This performance is significantly better than the DMPC algorithm, which considers neighbours as fixed-speed units. In this paper, the proposed method is implemented in sequential form. This algorithm uniquely predicts the trajectories of neighbouring UAVs, even in scenarios where there is an absence of inter-UAV communication. Such a feat allows UAVs to effectively navigate around static obstacles without the standard communication protocols, challenging the traditional norms of UAV navigation. The current model of this innovative algorithm operates in a sequential form. While it has showcased efficacy in simulations, its real-world applicability remains to be tested. As a next step, the method should evolve into a distributed form. The method should also be accompanied by real-world UAV testing, opening avenues for more extensive applications and refinements. Subsequently, [PERSON] et al. [PERSON] et al. (2018) introduce a predictive control methodology aimed at generating cooperative movement among heterogeneous agents working within constrained spaces, such as warehouses. The proposed method formulates a goal function and a dynamic model. This model amalgamates the state and inputs of all agents. Simultaneously, it incorporates constraints rooted in each agent's specifications, including aspects such as the size and limitations of the actuators. Collision avoidance is also achieved using an occupancy grid map to account for obstacles of any shape in the target area. The effectiveness of the proposed method was demonstrated in a numerical simulation of heterogeneous mobile robots operating in a warehouse model. The authors introduce an innovative approach characterized by the integration of a goal function and a dynamic model. This model seamlessly integrates the states and inputs of all involved agents, meticulously factoring in constraints associated with each agent's specific characteristics, such as size and actuator capabilities. A notable highlight is the adoption of an occupancy grid map, ensuring robust collision avoidance not only between agents but also with obstacles of varying shapes within the designated area. However, the method's efficacy has been validated solely through simulations; hence, testing in real-world scenarios is an imperative next step. Future work might explore refining this model for different confined environments or incorporating sophisticated sensing mechanisms to enhance agent collaboration. In another significant contribution to the field, [PERSON] et al. [PERSON] et al. (2018) present an algorithm designed for dynamic order picking in warehouse operations. This method, known as the Interventionist Routing Algorithm (IRA), is geared towards devising optimal routes for order pickers. The algorithm was modified to allow an operator to start an order-picking route from inside an aisle. This change required the development of new initiation procedures and the identification of new arc configurations to allow the forklift to leave the aisle. A new type of travel zone was introduced to increase the flexibility of the algorithm. The algorithm now uses two types of travel areas, One-Way (OW) and Round-Trip (RT), which are defined based on the relative locations of the picker, magazine, and all other retrieved items. The Round-Trip area was previously studied by [PERSON] and [PERSON] 1983. In addition, seven new PRS equivalence classes were added in addition to the six PRS equivalence classes previously proposed for the Round-Trip area. Six of these were proposed for the newly introduced One-Way area, while a seventh was added to the Round-Trip area. Finally, to adapt to the new One-Way areas and PRS equivalency classes, five additional tables were developed for route construction procedures. Moreover, two tables from [PERSON] and [PERSON]'s work were altered by the addition of one row each. Moreover, based on the performed simulations, the algorithm scalability was confirmed.