Optimizing Parameters for Uncertain Execution and Rescheduling Robustness

Generating and scheduling activities is particularly challenging when considering both consumptive resources and complex resource interactions such as time-dependent resource usage. We present three methods of determining valid temporal placement intervals for an activity in a temporally grounded plan in the presence of such constraints. We introduce the Max Duration and Probe algorithms which are sound, but incomplete, and the Linear algorithm which is sound and complete for linear rate resource consumption. We apply these techniques to the problem of scheduling awake and asleep episodes for a planetary rover where the awake durations are affected by scheduled activities. We demonstrate how the Probe algorithm performs competitively with the Linear algorithm given an advantageous problem space and well-defined heuristics. We show that the Probe and Linear algorithms outperform the Max Duration algorithm empirically. We then present the runtime differences between the three algorithms. The Probe algorithm is currently base-lined for use in the onboard scheduler for NASA's next planetary rover, the Mars 2020 rover.

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“…When the wakeup and shutdown durations are extended, the difference becomes even more clear. tivity priorities are set, see (Chi et al 2019).…”

Section: Resultsmentioning

confidence: 99%

Scheduling with Complex Consumptive Resources for a Planetary Rover

Chi

Chien

Agrawal

2020

ICAPS

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“…Because the scheduler does not backtrack, the order in which it considers activities to schedule can substantially affect schedule quality. To address this issue, a ground-based meta-search on activity priority (which determines the order in which activities are considered for scheduling) (37) is performed that attempts to set activity priorities that will perform well in all expected invocations of the scheduler. In this meta-search, various execution traces are hypothesized, and scheduler invocations with alternative activity priority settings are evaluated against these scheduler invocations to find a set of activity priorities that performs well on average over the potential executions (and therefore scheduler invocations).…”

Section: Onboard Plannermentioning

confidence: 99%

Autonomous robotics is driving Perseverance rover’s progress on Mars

et al. 2023

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NASA’s Perseverance rover uses robotic autonomy to achieve its mission goals on Mars. Its self-driving autonomous navigation system (AutoNav) has been used to evaluate 88% of the 17.7-kilometer distance traveled during its first Mars year of operation. Previously, the maximum total autonomous distance evaluated was 2.4 kilometers by the Opportunity rover during its 14-year lifetime. AutoNav has set multiple planetary rover records, including the greatest distance driven without human review (699.9 meters) and the greatest single-day drive distance (347.7 meters). The Autonomous Exploration for Gathering Increased Science (AEGIS) system analyzes wide-angle imagery onboard to autonomously select targets for observations by the SuperCam instrument, a multimode sensor suite capable of millimeter-scale geochemical and mineralogical analysis. AEGIS enables observations of scientifically interesting targets during or immediately after long drives without the need for ground communication. OnBoard Planner (OBP) is a scheduling capability planned for operational use in September 2023 that has the potential to reduce energy usage by up to 20% and complete drive and arm-contact science campaigns in 25% fewer days on Mars. This paper presents an overview of the AutoNav, AEGIS, and OBP capabilities used on Perseverance.

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“…The M2020 Perseverance rover also plans to fly an onboard planner (Rabideau and Benowitz 2017) to reduce lost productivity from following fixed time conservative plans (Gaines and et al 2016). Like the planning approach we propose in this paper, the M2020 planning architecture also relies on rescheduling and flexible execution (Agrawal et al 2021a), ground-based compilation (Chi et al 2019), heuristics (Chi, Chien, and Agrawal 2020), and very limited handling of planning contingencies (Agrawal et al 2021b). However, it uses a non-backtracking planner, which limits its ability to optimize plans and the M2020 flight software does not support utility discovery.…”

Section: Related Workmentioning

confidence: 99%

Analyzing the Efficacy of Flexible Execution, Replanning, and Plan Optimization for a Planetary Lander

Wang

Russino

Basich

et al. 2022

ICAPS

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Plan execution in unknown environments poses a number of challenges: uncertainty in domain modeling, stochasticity at execution time, and the presence of exogenous events. These challenges motivate an integrated approach to planning and execution that is able to respond intelligently to variation. We examine this problem in the context of the Europa Lander mission concept, and evaluate a planning and execution framework that responds to feedback and task failure using two techniques: flexible execution and replanning with plan optimization. We develop a theoretical framework to estimate gains from these techniques, and we compare these predictions to empirical results generated in simulation. These results indicate that an integrated approach to planning and execution leveraging flexible execution, replanning, and utility maximization shows significant promise for future tightly-constrained space missions that must address significant uncertainty.

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Optimizing Parameters for Uncertain Execution and Rescheduling Robustness

Cited by 10 publications

References 15 publications

Scheduling with Complex Consumptive Resources for a Planetary Rover

Scheduling with Complex Consumptive Resources for a Planetary Rover

Autonomous robotics is driving Perseverance rover’s progress on Mars

Analyzing the Efficacy of Flexible Execution, Replanning, and Plan Optimization for a Planetary Lander

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