Enhancing Rover Teleoperation on the Moon With Proprioceptive Sensors and Machine Learning Techniques - Coloma, Sofia; Martinez, Carol; Yalcn, Bars Can; Olivares-Mendez, Miguel A. (IEEE robotics and automation letters, 2022)
Geological formations, environmental conditions, and soil mechanics frequently generate undesired effects on rovers' mobility, such as slippage or sinkage. Underestimating these undesired effects may compromise the rovers' operation and lead to a premature end of the mission. Minimizing mobility risks becomes a priority for colonising the Moon and Mars. However, addressing this challenge cannot be treated equally for every celestial body since the control strategies may differ; e.g. the low latency Earth-Moon communication allows constant monitoring and controls, something not feasible on Mars. This letter proposes a Hazard Information System (HIS) that estimates the rover's mobility risks (e.g. slippage) using proprioceptive sensors and Machine Learning (supervised and unsupervised). A Graphical User Interface was created to assist human-teleoperation tasks by presenting mobility risk indicators. The system has been developed and evaluated in the lunar analogue facility (LunaLab) at the University of Luxembourg. A real rover and eight participants were part of the experiments. Results demonstrate the benefits of the HIS in the decision-making processes of the operator's response to overcome hazardous situations.