Hierarchical Multi-Channel Tip-Over Prediction via Isolated Temporal Models for Planetary Rover Safety
Overview
This research introduces a hierarchical multi-channel tip-over prediction framework designed for planetary rovers operating in unstructured environments. By integrating a multi-horizon Temporal Convolutional Network (TCN) to learn instability patterns from inertial measurements with a Gated Recurrent Unit (GRU)-based forward dynamics model, the system detects both gradual evolution and abrupt disturbances. Deployed as an Orocos Real-Time Toolkit component within the Rock robotics framework at 100 Hz, experiments on IMU samples across heterogeneous terrains demonstrate 97.58% event prediction coverage with a 1.82s median lead time, outperforming individual channels. The results validate that isolated temporal models combined through hierarchical decision fusion provide a robust solution for real-time rover safety.
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Reported quantity · seconds median lead time: 1.82 other · Basis not reported
“terrains demonstrate 97.58% event prediction coverage with a 1.82s median lead time, outperforming individual prediction channels. The results validate that isolated temporal models combined through hierarchical decision fusion provide a robust solution for real-time rover safety in uncertain terrain.”
Exact source · revision 1Source owner not reported
Reported quantity · frequency of operation: 100 other · Basis not reported
“rated through decision-level fusion and priority-based arbitration to generate graded safety actions, including early warning and emergency braking. The proposed system is deployed as an Orocos Real-Time Toolkit (RTT)-based safety component within the Rock robotics framework, operating at 100 Hz on a planetary rover software stack. Experiments on Inertial Measurement Unit (IMU) samples collected across heterogeneous”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalHierarchical Multi-Channel Tip-Over Prediction via Isolated Temporal Models for Planetary Rover Safety
This research introduces a hierarchical multi-channel tip-over prediction framework designed for planetary rovers operating in unstructured environments. By integrating a multi-horizon Temporal Convolutional Network (TCN) to learn instability patterns from inertial measurements with a Gated Recurrent Unit (GRU)-based forward dynamics model, the system detects both gradual evolution and abrupt disturbances. Deployed as an Orocos Real-Time Toolkit component within the Rock robotics framework at 100 Hz, experiments on IMU samples across heterogeneous terrains demonstrate 97.58% event prediction coverage with a 1.82s median lead time, outperforming individual channels. The results validate that isolated temporal models combined through hierarchical decision fusion provide a robust solution for real-time rover safety.
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