OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Ponzoni Carvalho Chanel, Caroline and Albore, Alexandre and T'Hooft, Jorrit and Lesire, Charles and Teichteil-Königsbuch, Florent AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics. (2019) Autonomous Robots, 43 (1). 37-62. ISSN 0929-5593

(Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader

Official URL: https://doi.org/10.1007/s10514-018-9703-z


Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop.

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Other partners > Airbus (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
French research institutions > Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE)
Other partners > IRT Saint Exupéry - Institut de Recherche Technologique (FRANCE)
Laboratory name:
Deposited On:16 Feb 2018 09:55

Repository Staff Only: item control page