Prediction-Based Tip Over Prevention for Planetary Exploration Rovers

Abstract :

This study presents a novel prediction-based system designed to prevent tip-over incidents on planetary exploration rovers, thereby enhancing their operational safety and reliability. Planetary rovers, critical for space exploration missions, must navigate through uneven surfaces and terrains with undefined interaction properties. Furthermore, the next planetary rovers will need to traverse harsher environments than those of the current and previous missions, such as steep craters or caves, since relevant scientific data is to be acquired in such locations [1]. This poses a challenge for unmanned missions, significantly increasing the risk of tipping over, even in remote-controlled operations. The proposed system employs linear accelerations and angular velocities measured by the accelerometer and the gyroscope of the Inertial Measurements Unit to monitor the rover's stability while navigating the environment. By leveraging deep learning algorithms, the system generates predictions in real-time. These predictions are compared with the sensor measurements to identify potential abnormal situations, such as tip-over scenarios. Since the tip-overs are forecasted in real-time, the system provides the possibility to adjust the rover's motion to prevent failure. The efficacy of this prediction-based approach is validated through simulations and field tests, demonstrating its capability to reduce the incidence of tip-overs under various challenging conditions. The integration of this system aims to extend the operational lifespan of rovers, optimize mission outcomes, and enhance the overall safety of planetary exploration missions.

Keywords :

tip-over prediction, rover instability, deep learning models, sequence modeling, rover safety, inertial measurement units (IMUs), terrain traversal