Improving Prediction of Traversability for Planetary Rovers Using Thermal Imaging
Creators: name: Cunningham, Christopher
Publisher: name: Carnegie Mellon University
Year: 2017
Subjects: subject: 80101 Adaptive Agents and Intelligent Robotics
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: The most significant mobility challenges that planetary rovers encounter are compounded by loose, granular materials that cause slippage and sinkage on slopes or are deep enough to entrap a vehicle. The inability of current technology to detect loose terrain hazards has caused significant delays for rovers on both the Moon and Mars and, most notably, contributed to Spirit’s permanent entrapment in soft sand on Mars. Classical, vision-based traversability prediction methods are inherently limited by only measuring surface appearance, which is not necessarily correlated to subsurface bulk mechanical properties that influence mobility, such as bulk density and particle size distribution. The inherent difficulty of estimating traversability is compounded by the conservative nature of planetary rover operations. Mission operators actively avoid potentially hazardous regions. Hence there is little intentional driving in weak soil and limited data from which to train a feature-intensive, vision-based algorithm. Instead, this thesis leverages thermal physics to improve traversability prediction for rovers operating in planetary terrain. Unlike visual appearance, the thermal inertia of a granular material is directly influenced by its bulk physical properties both at and below the surface. A robot can use a thermal camera to observe the surface temperatures of terrain, which are then fit to an analytical model to find thermal inertia. These measurements can then be used to distinguish between safe and hazardous granular materials on Mars and to a lesser extent on the Moon. This research investigates how to use thermal inertia to improve traversability prediction as well as when and where it applies. Both natural fluxes and laser heating are leveraged to produce a transient temperature response, from which a robot can estimate the thermal properties of terrain. Natural heat sources (i.e. solar and atmospheric fluxes) are shown to be more effective because they are uniform and have longer illumination periods, which results in measurements over the whole surface that are influenced by materials at depths of several centimeters. Terrestrial experiments show that the ability of this approach to predict traversability depends both on time of day and length of observation, with longer, nighttime measurements resulting in the fewest errors. Experimental results using in-situ data from the Curiosity rover demonstrate the ability of thermal inertia measurements to improve slip prediction accuracy on Mars by reducing cross-validation slip prediction error by 26%. Simulations show that at most times of day, thermal imaging could also identify hazards caused by thin duricrust over deep sand, which was the situation that trapped Spirit on Mars. Results also show that there is a measurable difference in temperature between nominal and loose regolith samples on the Moon, even in permanently shadowed polar craters. This effect is most consistent at night in the absence of solar radiation and generally causes temperature differences between 2 and 3 K. Though it certainly does not account for all of the intricacies of a rover’s interaction with terrain, thermal inertia represents a single measurement that can improve traversability prediction, especially at night when visual approaches fail.