# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Deep Learning Methods
The proposed model solves the important problem of trajectory prediction by enforcing dynamic constraints and incorporating environmental information, which is crucial for safe and socially aware robot navigation. The success of Trajectron++ in a variety of real-world scenarios demonstrates its potential to enhance the performance of interactive human-robot systems, particularly self-driving cars. The model has been tested on ETH, UCY, and nuScenes datasets. The Trajectron++ model is distinctively crafted to integrate effortlessly with robot planning and control frameworks, even offering the capability to condition predictions based on ego-agent motion plans. While the authors have not explicitly highlighted any limitations, it is worth noting that many deep models often face challenges when applied to real-world scenarios. In the future, the potential of Trajectron++ could be amplified by incorporating diverse data sources, enhancing its alignment with robotic systems, and capitalizing on its ability to generate comprehensive probability distributions for sophisticated robotic tasks. In their paper, [PERSON] et al. [69] proposed an NSP-SFM model. This solution combines model-based and model-free approaches using a new Neural Differential Equation model. The NSP-SFM is a deep neural network which employs an explicit physics model featuring learnable parameters. This setup provides a potent inductive bias in the modelling of pedestrian behaviour. Simultaneously, the remaining parts of the network exhibit a strong capability in matching data, estimating system parameters, and modelling the stochasticity of dynamics. The authors compared NSP with 15 state-of-the-art deep learning methods on six datasets and improved state-of-the-art performance by 5.56-70%. They also showed that NSP has better generalization in predicting reliable trajectories in high-density scenarios. In addition, the authors showed that the physics model in NSP can provide reliable explanations for pedestrian behaviour, unlike black-box deep learning models. Validation of this solution was performed on ETH/UCY and SDD datasets. The NSP-SFM model introduces a novel blend of model-based and model-free techniques through a Neural Differential Equation framework. This innovation effectively models pedestrian behaviour by merging an explicit, learnable physics model with a deep neural network's data-fitting prowess. It outperforms 15 other deep learning methods and offers better generalizability in high-density scenarios. However its limitation lies in oversimplifying humans as 2D particles, neglecting the complexities of real-world human dynamics. Future enhancements may delve into more intricate human behavior models, adapt to high-density crowd scenarios, and incorporate learning-based collision detection techniques. A notable work fulfilling the review condition and offering a profound approach was provided by [PERSON] et al. [70]. This research introduced a framework that aims to predict human plans and trajectories, a capability deemed highly valuable for effective human-robot collaboration. The authors of the paper emphasize that human plan and trajectory prediction are closely related, which is in line with the observations of other researchers (e.g., Ynet, NSP). The focus of the research paper is mainly on desktop assembly; however, the solution can be successfully applied in other fields where there is a need to coordinate the work of humans and machines. The framework system has been tested on actual industrial machines, which guarantees its effectiveness and practical application. The components of the framework system are algorithms for plan detection, trajectory prediction, and a robot behaviour planner. The plan detection algorithm is based on four components: trajectory prediction using LSTM neural networks, user target classification, user action classification using the Bayes algorithm, and correction of actual posteriori actions.