Learning and Reconstruction of Mobile Robot Trajectories with LSTM Autoencoders: A Data-Driven Framework for Real-World Deployment

Accurate trajectory learning and reconstruction represent a core challenge in mobile robotics, particularly in environments affected by sensor noise, drift, and incomplete data. Addressing this challenge is essential for reliable navigation and motion control in real-world Internet of Robotic Things (IoRT) systems. This paper presents a data-driven framework for learning and reconstructing mobile robot trajectories using LSTM autoencoders. Trajectory data were collected from both simulation and real-world experiments with a Unitree GO1 quadruped robot, preprocessed through normalization, sequence padding, and trajectory boundary flags, and then used to train recurrent neural network models. The proposed architecture employs bidirectional LSTM layers and a custom loss function combining reconstruction, velocity, and boundary terms to improve trajectory stability. Experimental results show stable reconstruction accuracy across simulated and real-world datasets, with the position RMSE reduced from 0.92 m to 0.60 m and the yaw MAE improved from 0.49 rad to 0.17 rad on the most complex trajectory. The evaluation was conducted in controlled indoor conditions and offline mode, which defines the current scope of validation. Future work will extend the analysis to larger and more diverse environments and investigate extensions such as attention mechanisms, sensor fusion, and online learning to enhance adaptability in real-world deployment.