The Mars 2020 OnBoard Planner: Increasing autonomy mission productivity of the Perseverance rover

Operators of planetary rovers, under strict energy and thermal constraints, face substantial challenges in maintaining high levels of rover productivity because of conservative ground-based planning and long communication latencies. In October 2023, to overcome these limits and increase science return, the Mars 2020 mission deployed OnBoard Planner into Perseverance rover operations. OnBoard Planner uses artificial intelligence constraint-based search to dynamically construct and modify activity schedules on board, adapting to real-time changes in rover state and martian environmental conditions. By replacing conservative ground predictions with real-time resource tracking, the system dynamically adjusts heating durations, reclaims unused schedule margin, and inserts opportunistic activities when extra resources become available. Here, we report on OnBoard Planner's performance on Mars across more than 2 years of surface operations. The results demonstrate that OnBoard Planner increased discretionary rover activity by 10.5% overall while also improving the thermal and temporal conditions for certain types of scientific observations. Furthermore, the system achieved these productivity gains without increasing ground operations timelines. These findings are part of why OnBoard Planner has successfully become a standard part of Mars 2020 operations and demonstrate how dynamic onboard autonomy can enable resource-efficient operations for complex robotic systems in deep-space environments.

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Publication Details

Journal
Science Robotics
Published
2026-10-28
DOI
https://doi.org/10.1126/scirobotics.aeh2947
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
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article

The Mars 2020 OnBoard Planner: Increasing autonomy mission productivity of the Perseverance rover

Steve Chien, Stephen R. Kuhn, Daniel M. Gaines, Gregg Rabideau et al.
Science Robotics
AI-based Problem Solving and Planning
article

The Mars 2020 OnBoard Planner: Increasing autonomy mission productivity of the Perseverance rover

Steve Chien, Stephen R. Kuhn, Daniel M. Gaines, Gregg Rabideau, Elyse Moffi, Vincent Wong, Edward Benowitz
article en

Abstract

Operators of planetary rovers, under strict energy and thermal constraints, face substantial challenges in maintaining high levels of rover productivity because of conservative ground-based planning and long communication latencies. In October 2023, to overcome these limits and increase science return, the Mars 2020 mission deployed OnBoard Planner into Perseverance rover operations. OnBoard Planner uses artificial intelligence constraint-based search to dynamically construct and modify activity schedules on board, adapting to real-time changes in rover state and martian environmental conditions. By replacing conservative ground predictions with real-time resource tracking, the system dynamically adjusts heating durations, reclaims unused schedule margin, and inserts opportunistic activities when extra resources become available. Here, we report on OnBoard Planner's performance on Mars across more than 2 years of surface operations. The results demonstrate that OnBoard Planner increased discretionary rover activity by 10.5% overall while also improving the thermal and temporal conditions for certain types of scientific observations. Furthermore, the system achieved these productivity gains without increasing ground operations timelines. These findings are part of why OnBoard Planner has successfully become a standard part of Mars 2020 operations and demonstrate how dynamic onboard autonomy can enable resource-efficient operations for complex robotic systems in deep-space environments.

Science RoboticsVol. 11(119)
Jet Propulsion Laboratory (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
AI-based Problem Solving and Planning
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The Mars 2020 OnBoard Planner: Increasing autonomy mission productivity of the Perseverance rover — Steve Chien, Stephen R. Kuhn, et al. · Science Robotics (2026) | TGRS Research Map | TGRS