# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Deep Learning Methods
However, a limitation is evident in the deep warehouse environment used for testing, which does not mirror real-world systems perfectly. Additionally, while the DVAE-2 model showcases efficiency, model-free algorithms, despite their potential for better performance with unlimited sampling, present challenges in training. Looking ahead, there is a pressing need to ensure agents operate within safety boundaries, especially when confronted with unforeseen events such as fires or agent collisions. Another intriguing avenue for future exploration is the efficacy of agents with non-stationary policies in multi-agent environments. Another paper by [PERSON] et al. [61] proposes a novel PECNet neural network architecture. PECNet is a neural network architecture that uses both past history and truthful endpoint to train a Variational Autoencoder (VAE) for multimodal endpoint inference. The network consists of three main components: a social pooling module, a predictor network, and a VAE. During training, real trajectory targets are used. The social pooling module takes the past history and ground truth endpoint, along with endpoint sampling, and encodes them into a latent representation. The social pooling module is equipped with a block diagonal social mask, which encodes spatial-temporal relationships between agents. The predictor network takes the latent representation from the social linking module and generates a predicted trajectory for each agent. The predictor network is conditioned on sampled endpoints and the social pooling module. VAE is used to infer multimodal endpoints, allowing the network to predict multiple possible future trajectories for each agent. The VAE is trained using both a truth endpoint and a sampled endpoint to learn the underlying trajectory distribution. The model was validated on the ETH/UCY and Stanford Drone (SDD) datasets. Overall, PECNet is a powerful network architecture for multi-agent, multimodal trajectory forecasting that uses both past history and ground truth endpoints to generate accurate and differentiated forecasts. The innovative aspect of the article is the introduction of the PECNet neural network architecture, a sophisticated design tailored for multi-agent, multimodal trajectory forecasting. The authors have also introduced the innovative \"truncation trick\" for trajectory prediction, a method that adjusts diversity for performance without the need for retraining. However, the model's reliance on real trajectory targets during training could pose challenges in scenarios where such data are limited or unavailable.With its state-of-the-art performance across multiple datasets, future endeavours could delve deeper into refining the model's components and exploring its applicability in even more diverse settings. Nevertheless, the application of PECNet in real applications is problematic, as indicated by [PERSON] and [PERSON] [62]. In this work, the authors improve the PECNet model, which was originally highly overtrained to training data, by which it could not be applied to a real-world application. They also reported that adding noise to the data completely destroyed the PECNet learning process. That made it unable to be applied to problems where the data contain noise, e.g., UWB sensors have an error of several centimetres. The improvements that the authors made to PECNet as a separate SIREN model are as follows: (1) They used a different method for optimization--used CMA-ES instead of ADAM. (2) They implemented cyclic annealing to stabilize the variational training of the autoencoder. (3) Another generative model has been added to produce a multimodal model output. This result was a confidence metric for better prediction and control, i.e., they replaced the standard multilayer perceptrons (MLPs) on the sinusoidal representation network (SIREN).