Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)

Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software

Investigating Habitability with an Integrated Rock-Climbing Robot and Astrobiology Instrument Suite

A prototype rover carrying an astrobiology payload was developed and deployed at analog field sites to mature generalized system architectures capable of searching for biosignatures in extreme terrain across the Solar System. Specifically, the four-legged Limbed Excursion Mechanical Utility Robot (LEMUR) 3 climbing robot with microspine grippers carried three instruments: A micro-X-ray fluorescence instrument based on the Mars 2020 mission's Planetary Instrument for X-ray Lithochemistry provided elemental chemistry; a deep-ultraviolet fluorescence instrument based on Mars 2020's Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals mapped organics in bacterial communities on opaque substrates; and a near-infrared acousto-optic tunable filter-based point spectrometer identified minerals and organics in the 1.6-3.6 μm range. The rover also carried a light detection and ranging and a color camera for both science and navigation. Combined, this payload detects astrobiologically important classes of rock components (elements, minerals, and organics) in extreme terrain, which, as demonstrated in this work, can reveal a correlation between textural biosignatures and the organics or elements expected to preserve them in a habitable environment. Across >10 field tests, milestones were achieved in instrument operations, autonomous mobility in extreme terrain, and system integration that can inform future planetary science mission architectures. Contributions include (1) system-level demonstration of mock missions to the vertical exposures of Mars lava tube caves and Mars canyon walls, (2) demonstration of multi-instrument integration into a confocal arrangement with surface scanning capabilities, and (3) demonstration of automated focus stacking algorithms for improved signal-To-noise ratios and reduced operation time.</p