Search for: All records

ABSTRACT Recently, there has been a surge of international interest in extraterrestrial exploration targeting the Moon, Mars, the moons of Mars, and various asteroids. This contribution discusses how current state‐of‐the‐art Earth‐based testing for designing rovers and landers for these missions currently leads to overly optimistic conclusions about the behavior of these devices upon deployment on the targeted celestial bodies. The key misconception is that gravitational offset is necessary during theterramechanicstesting of rover and lander prototypes on Earth. The body of evidence supporting our argument is tied to a small number of studies conducted during parabolic flights and insights derived from newly revised scaling laws. We argue that what has prevented the community from fully diagnosing the problem at hand is the absence of effective physics‐based models capable of simulating terramechanics under low‐gravity conditions. We developed such a physics‐based simulator and utilized it to gauge the mobility of early prototypes of the Volatiles Investigating Polar Exploration Rover. This contribution discusses the results generated by this simulator, how they correlate with physical test results from the NASA‐Glenn SLOPE lab, and the fallacy of the gravitational offset in rover and lander testing. The simulator, which is open‐source and publicly available, also supports studies for in situ resource utilization activities, for example, digging, bulldozing, and berming, in low‐gravity environments. 

Abstract In robotics, simulation has the potential to reduce design time and costs, and lead to a more robust engineered solution and a safer development process. However, the use of simulators is predicated on the availability of good models. This contribution is concerned with improving the quality of these models via calibration, which is cast herein in a Bayesian framework. First, we discuss the Bayesian machinery involved in model calibration. Then, we demonstrate it in one example: calibration of a vehicle dynamics model that has low degree-of-freedom (DOF) count and can be used for state estimation, model predictive control, or path planning. A high fidelity simulator is used to emulate the “experiments” and generate the data for the calibration. The merit of this work is not tied to a new Bayesian methodology for calibration, but to the demonstration of how the Bayesian machinery can establish connections among models in computational dynamics, even when the data in use is noisy. The software used to generate the results reported herein is available in a public repository for unfettered use and distribution.