Ruqayya Alhammadi | Searcharxiv

arXiv2024

SlipNet: Enhancing Slip Cost Mapping for Autonomous Navigation on Heterogeneous and Deformable Terrains

Autonomous space rovers face significant challenges when navigating deformable and heterogeneous terrains due to variability in soil properties, which can lead to severe wheel slip, compromising navigation efficiency and increasing the risk of entrapment. To address this problem, we introduce SlipNet, a novel approach for predicting wheel slip in segmented regions of diverse terrain surfaces without relying on prior terrain classification. SlipNet employs dynamic terrain segmentation and slip assignment techniques on previously unseen data, enhancing rover navigation capabilities in uncertain environments. We developed a synthetic data generation framework using the high-fidelity Vortex Studio simulator to create realistic datasets that replicate a wide range of deformable terrain conditions for training and evaluation. Extensive simulation results demonstrate that our model, combining DeepLab v3+ with SlipNet, significantly outperforms the state-of-the-art TerrainNet method, achieving lower mean absolute error (MAE) across five distinct terrain samples. These findings highlight the effectiveness of SlipNet in improving rover navigation in challenging terrains.

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arXiv2024

Physics-Informed LSTM-Based Delay Compensation Framework for Teleoperated UGVs

Bilateral teleoperation of low-speed Unmanned Ground Vehicles (UGVs) on soft terrains is crucial for applications like lunar exploration, offering effective control of terrain-induced longitudinal slippage. However, latency arising from transmission delays over a network presents a challenge in maintaining high-fidelity closed-loop integration, potentially hindering UGV controls and leading to poor command-tracking performance. To address this challenge, this paper proposes a novel predictor framework that employs a Physics-informed Long Short-Term Memory (PiLSTM) network for designing bilateral teleoperator controls that effectively compensate for large delays. Contrasting with conventional model-free predictor frameworks, which are limited by their linear nature in capturing nonlinear and temporal dynamic behaviors, our approach integrates the LSTM structure with physical constraints for enhanced performance and better generalization across varied scenarios. Specifically, four distinct predictors were employed in the framework: two compensate for forward delays, while the other two compensate for backward delays. Due to their effectiveness in learning from temporal data, the proposed PiLSTM framework demonstrates a 26.1\ improvement in delay compensation over the conventional model-free predictors for large delays in open-loop case studies. Subsequently, experiments were conducted to validate the efficacy of the framework in close-loop scenarios, particularly to compensate for the real-network delays experienced by teleoperated UGVs coupled with longitudinal slippage. The results confirm the proposed framework is effective in restoring the fidelity of the closed-loop integration. This improvement is showcased through improved performance and transparency, which leads to excellent command-tracking performance.

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