Prediction-Based Tip-Over Prevention for Planetary Exploration Rovers

This study presents a deep learning-based prediction system with an elevated approach to prevent tip-over incidents on planetary exploration rovers, enhancing their operational safety and reliability. Planetary rovers, critical for space exploration missions, must navigate through uneven surfaces and terrains with undefined interaction properties. Future planetary rovers must navigate harsher terrains, like steep craters and caves, to access critical scientific data, significantly risking tip-over in any state of operational control. The proposed system employs linear accelerations and angular velocities measured by the accelerometer and the gyroscope of the Inertial Measurements Unit (IMU) to monitor the rover’s dynamic behavior and stability while navigating the environment. By leveraging deep learning algorithms, the system evaluates predictions and true measurements in real time to identify potential tip-overs. Additionally, 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 on two robotic platforms, the Asguard v4 and Coyote 3 rovers, 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.