Physics-informed LSTM-based delay compensation framework for high-fidelity teleoperation of wheeled mobile robots

Bilateral teleoperation of wheeled mobile robots (WMRs) traversing soft terrains with interaction force feedback is crucial for applications like lunar exploration, as it help regulates travel reduction and path deviation. However, network-induced communication delays present a challenge in achieving high-fidelity closed-loop integration, degrading slippage awareness and command-tracking performance. To address this, a physics-informed long short-term memory (PiLSTM)-based predictor framework is proposed to predicts intended commands and anticipated slippage feedback in WMR teleoperation systems under large delays. Conventional model-free predictors often demonstrate significant prediction errors when compensating for large delays in complex dynamics systems due to their limited adaptability and generalizability. In contrast, the proposed PiLSTM predictor integrates an LSTM network with time-delay dynamics and a feedback mechanism to better capture strong nonlinear and temporal dynamics for improved adaptability and generalizability while ensuring accurate prediction. A series of human-in-the-loop experiments for a track-following task were conducted to validate the effectiveness of the PiLSTM framework in compensating for the large delays, similar to those encountered in lunar exploration. Its performance was compared against first-order time-delay (FOD) and wave variable transformation (WV) approaches for delay compensation. The results demonstrate the robustness of the PiLSTM framework across various test scenarios, achieving up to 29.7% improvement in prediction accuracy compared to the FOD framework. Furthermore, the proposed PiLSTM-based teleoperation system achieved significantly improved tracking performance and force transparency compared to FOD- and WV-based systems, ensuring higher fidelity and more robust closed-loop integration. These findings highlight the PiLSTM framework's adaptability and generalizability for complex teleoperation systems. A supplementary video is available at https://youtu.be/dMVLEV7qTvE.