Nicola Policella

Mexar2: AI Solves Mission Planner Problems

Deep-space missions carry an ever larger set of different and complementary onboard payloads. Each payload generates data, and synthesizing it for optimized downlinking is one way to reduce the ratio of mission costs to science return. This is the main role of the Mars-Express scheduling architecture (Mexar2), an Al-based tool in daily use on the Mars-Express mission since February 2005. Mexar2 supports space mission planners continuously as they plan data downlinks from the spacecraft to Earth. The tool lets planners work at a higher abstraction level while it performs low-level, often-repetitive tasks. It also helps them produce a plan rapidly, explore alternative solutions, and choose the most robust plan for execution. Additionally, planners can analyze any problems over multiple days and identify payload overcommitments that cause resource bottlenecks and increase the risk of data losses. Mexar2 has significantly increased the data return over the whole Mars-Express mission duration. Its effectively become a work companion for mission planners at the European Space Agencys European Space Operations Center (ESOC) in Darmstadt, Germany.

Solve-and-robustify

AbstractGoal separation is often a fruitful approach when solving complex problems. It provides a way to focus on relevant aspects in a stepwise fashion and hence bound the problem solving scope along a specific direction at any point. This work applies goal separation to the problem of synthesizing robust schedules. The problem is addressed by separating the phase of problem solution, which may pursue a standard optimization criterion (e.g., minimal makespan), from a subsequent phase of solution robustification in which a more flexible set of solutions is obtained and compactly represented through a temporal graph, called a Partial Order Schedule (

Validating scheduling approaches against executional uncertainty

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Iterative flattening search for resource constrained scheduling

). The key advantage of a

SKEYP: AI Applied to SOHO Keyhole Operations

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The RAXEM Tool on Mars Express - Uplink Planning Optimisation and Scheduling Using AI Constraint Resolution

is that it provides the capability to promptly respond to temporal changes (e.g., activity duration changes or activity start-time delays) and to hedge against further changes (e.g., new activities to perform or unexpected variations in resource capacity).On the one hand, the paper focuses on specific heuristic algorithms for synthesis of

Combining variants of iterative flattening search

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Boosting the Performance of Iterative Flattening Search

s, starting from a pre-existing schedule (hence the name Solve-and-Robustify). Different extensions of a technique called chaining, which progressively introduces temporal flexibility into the representation of the solution, are introduced and evaluated. These extensions follow from the fact that in multi-capacitated resource settings more than one

A novel ACO algorithm for dynamic binary chains based on changes in the system's stability

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Science Operations Pre-Planning & Optimization using AI constraint-resolution - the APSI Case Study 1

can be derived from a specific fixed-times solution via chaining, and carry out a search for the most robust alternative. On the other hand, an additional analysis is performed to investigate the performance gain possible by further broadening the search process to consider multiple initial seed solutions.A detailed experimental analysis using state-of-the-art rcpsp/max  benchmarks is carried out to demonstrate the performance advantage of these more sophisticated solve and robustify procedures, corroborating prior results obtained on smaller problems and also indicating how this leverage increases as problem size is increased.