Abstract

To fulfill their mission properly, planetary exploration rovers must often be able to travel long distances and traverse various terrain types. Some terrain types and topologies may present traversability challenges. In difficult situations, such as slopes and loose soil, rover wheel slip may increase to a level leading to entrapment risks. Autonomous slip detection allows a rover to detect potentially dangerous terrain and take precautions. While visual odometry slip estimation solutions exist, numerous external factors, such as luminance, haze and shadows, may negatively impact quality of imaging sensor data and consequently slip estimation. Visual odometry also requires significant computational resources. Previous studies have shown promise in the use of Machine Learning algorithms to process IMU-measured vibration data to detect and classify slip events. This research develops a low-latency and computationally efficient vibration-based system to detect wheel-terrain slip events for skid-steer rovers with modest hardware requirements.

To this end, vibration datasets corresponding to various wheel-terrain slip values are generated. A Husky rover with two Inertial Measurement Unit sensors is used in indoor and outdoor test environments. Slip is induced at specific values by mechanically constraining the rover to reduce the Actual Rover Speed below the Commanded Rover Speed. The vibration datasets are used to train and validate a Support Vector Machine classifier to differentiate abnormally high slip events from normal low slip. The training is done with various sensor outputs, sampling time, and sampling frequency. The performance of the system is then evaluated in order to find which combinations of parameters are effective and to qualify the trade-offs in performance which come with less ideal parameter values.