Scheduling scientific experiments for comet exploration

Abstract

The Rosetta/Philae mission was launched in 2004 by the European Space Agency (ESA). It is scheduled to reach the comet 67P/Churyumov-Gerasimenko in November 2014 after traveling more than six billion kilometers. The Philae module will then be separated from the orbiter (Rosetta) to attempt the first ever landing on the surface of a comet. If it succeeds, it will engage a sequence of scientific exploratory experiments on the comet. In this paper, we describe a constraint programming model for scheduling the different experiments of the mission. A feasible plan must satisfy a number of constraints induced by energetic resources, precedence relations on tasks, and incompatibility between instruments. Moreover, a very important aspect is related to the transfer (to the orbiter then to the Earth) of all the data produced by the instruments. The capacity of inboard memories and the limitation of transfers within visibility windows between lander and orbiter, make the transfer policy implemented on the lander CPU prone to data loss. We introduce a global constraint to handle data transfers. The purpose of this constraint is to ensure that data-producing tasks are scheduled in such a way that no data is lost. Thanks to this constraint and to the filtering rules we propose, mission control is now able to compute feasible plans in a few seconds for scenarios where minutes were previously often required. Moreover, in many cases, data transfers are now much more accurately simulated, thus increasing the reliability of the plans.

Enriching Solutions to Combinatorial Problems via Solution Engineering

“…Constraint programming is a declarative technology for solving combinatorial problems, successfully used over the last thirty years in internet commerce, telecommunications, transportation, network management, supply chain management, and many other fields. A recent noteworthy application is the robot-lab Philae that landed on the comet 67P/Churyumov-Gerasimenko to conduct a series of experiments, which were scheduled using constraint programming (Simonin et al 2015).…”

Constraint Programming-Based Job Dispatching for Modern HPC Applications

“…To evaluate the significance of our approach, we conducted an experimental study by simulating on-line job submission two HPC systems. We dispatched the jobs using PCP'20, PCP '19,HCP'19, and compared them in various aspects.…”

“…Constraint Programming (CP) is a paradigm for modelling and solving discrete optimization problems [17], with successful applications in science, business and industry. For instance, the scientific exploratory experiments of Philae, the first robot-lab that landed on the surface of a comet in 2014, were scheduled using CP [19]. Though CP has its roots in logic programming and the constraint satisfaction area of AI, the last decades have witnessed its fruitful crossfertilization with related disciplines, such as operations research, and with other areas of AI such as SAT, heuristic search, and more recently machine learning.…”

Conflict history based heuristic for constraint satisfaction problem solving

“…The Constraint Satisfaction Problem (CSP) is an important formalism in Artificial Intelligence (AI) which allows to model and efficiently solve problems that occur in various fields, both academic and industrial (e.g. [8,19,37,40]). A CSP instance is defined on a set of variables, which must be assigned in their respective finite domains.…”