The Mars 2020 Perseverance rover landed in Jezero crater on 18 February 2021. After a 100‐sol period of commissioning and the Ingenuity Helicopter technology demonstration, Perseverance began its first science campaign to explore the enigmatic Jezero crater floor, whose igneous or sedimentary origins have been much debated in the scientific community. This paper describes the campaign plan developed to explore the crater floor's Máaz and Séítah formations and summarizes the results of the campaign between sols 100–379. By the end of the campaign, Perseverance had traversed more than 5 km, created seven abrasion patches, and sealed nine samples and a witness tube. Analysis of remote and proximity science observations show that the Máaz and Séítah formations are igneous in origin and composed of five and two geologic members, respectively. The Séítah formation represents the olivine‐rich cumulate formed from differentiation of a slowly cooling melt or magma body, and the Máaz formation likely represents a separate series of lava flows emplaced after Séítah. The Máaz and Séítah rocks also preserve evidence of multiple episodes of aqueous alteration in secondary minerals like carbonate, Fe/Mg phyllosilicates, sulfates, and perchlorate, and surficial coatings. Post‐emplacement processes tilted the rocks near the Máaz‐Séítah contact and substantial erosion modified the crater floor rocks to their present‐day expressions. Results from this crater floor campaign, including those obtained upon return of the collected samples, will help to build the geologic history of events that occurred in Jezero crater and provide time constraints on the formation of the Jezero delta.

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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.

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