Abstract
The paper presents an approach for the robust plan execution in presence of consumable and continuous resources. Plan execution is a critical activity since a number of unexpected situations could prevent the feasibility of tasks to be accomplished; however, many robotic scenarios (e.g. in space exploration) disallow robotic systems to perform significant deviations from the original plan formulation. In order to both (i) preserve the “stability” of the current plan and (ii) provide the system with a reasonable level of autonomy in handling unexpected situations, an innovative approach based on task reconfiguration is presented. Exploiting an enriched action formulation grounding on the notion of execution modalities, ReCon replaces the replanning mechanism with a novel reconfiguration mechanism, handled by means of a CSP solver. The paper studies the system for a typical planetary rover mission and provides a rich experimental analysis showing that, when the anomalies refer to unexpected resources consumption, the reconfiguration is not only more efficient but also more effective than a plan adaptation mechanism. The experiments are performed by evaluating the recovery performances depending on constraints on computational costs.
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Notes
- 1.
The notion of alternative (sub)plans is also presented for (off-line) scheduling; for details see [1].
- 2.
The software is at disposal at http://www.emn.fr/z-info/choco-solver/, while the work has been presented in [23].
- 3.
To simplify the picture, we show in the rover’s status just a subset of the whole status variables.
- 4.
The slowdown command of the rover may be the consequence of a reactive supervisor, which operates as a continuous controller as shown in [21].
- 5.
- 6.
Alternative CSP conversions are possible; for instance see [2].
- 7.
- 8.
- 9.
The Choco Solver implements the state of the art algorithms for constraint programming and has already been used in space applications, see [6]. Choco can be downloaded at http://www.emn.fr/z-info/choco-solver/.
- 10.
Experiments have run on a 2.53 GHz Intel(R) Core(TM)2 Duo processor with 4 GB.
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Acknowledgements
We would like to thank the Choco’s team for making freely available the CSP solver, Joerg Hoffman for the Metric-FF planning system as well as Alfonso Gerevini, Alessandro Saetti and Ivan Serina for the LPG-ADAPT system.
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Scala, E., Micalizio, R., Torasso, P. (2015). ReCon: An Online Task ReConfiguration Approach for Robust Plan Execution. In: Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2014. Lecture Notes in Computer Science(), vol 8946. Springer, Cham. https://doi.org/10.1007/978-3-319-25210-0_16
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