The Mars 2020 mission requires human experts to send path-finding instructions for the Perseverance rover to execute upon the unknown surfaces of Mars. Deciding which instructions minimize risk to the rover requires human planners to simulate rover movements, but the presence of small, unseen rocks creates uncertainty about risks to the rover.
Mars Rover Planners are studying the potential for AutoNav Monte Carlo simulations to help them assess the risks of extended automatic navigation over challenging topography.
We present GRIT, a tool for Rover planners to visualize the results of a large set of Monte Carlo path simulations, interact and filter the simulations enabling in depth investigation of the probability of runs incurring unnecessary costs. Further capabilities to investigate failed mission simulations can lead to better intuition of the overall terrain and can even foment the marking of explicit keep-out zones to help lower probability of rover mission failure.