More knowledge on the table:: Planning with space, time and resources for robots

Masoumeh Mansouri; Federico Pecora

doi:10.1109/ICRA.2014.6906923

More knowledge on the table: Planning with space, time and resources for robots

Masoumeh Mansouri, Federico Pecora

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

Abstract

AI-based solutions for robot planning have so far focused on very high-level abstractions of robot capabilities and of the environment in which they operate. However, to be useful in a robotic context, the model provided to an AI planner should afford both symbolic and metric constructs; its expressiveness should not hinder computational efficiency; and it should include causal, spatial, temporal and resource aspects of the domain. We propose a planner grounded on well-founded constraint-based calculi that adhere to these requirements. A proof of completeness is provided, and the flexibility and portability of the approach is validated through several experiments on real and simulated robot platforms

Original language	English
Title of host publication	2014 IEEE International Conference on Robotics and Automation (ICRA)
Publisher	IEEE Computer Society Press
Pages	647-654
ISBN (Electronic)	9781479936854
DOIs	https://doi.org/10.1109/ICRA.2014.6906923
Publication status	Published - 1 May 2014
Event	2014 IEEE International Conference on Robotics and Automation (ICRA) - Hong Kong, China Duration: 31 May 2014 → 7 Jun 2014

Conference

Conference	2014 IEEE International Conference on Robotics and Automation (ICRA)
Country/Territory	China
City	Hong Kong
Period	31/05/14 → 7/06/14

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10.1109/ICRA.2014.6906923

http://ieeexplore.ieee.org/document/6906923/

Cite this

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title = "More knowledge on the table:: Planning with space, time and resources for robots",

abstract = "AI-based solutions for robot planning have so far focused on very high-level abstractions of robot capabilities and of the environment in which they operate. However, to be useful in a robotic context, the model provided to an AI planner should afford both symbolic and metric constructs; its expressiveness should not hinder computational efficiency; and it should include causal, spatial, temporal and resource aspects of the domain. We propose a planner grounded on well-founded constraint-based calculi that adhere to these requirements. A proof of completeness is provided, and the flexibility and portability of the approach is validated through several experiments on real and simulated robot platforms",

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N2 - AI-based solutions for robot planning have so far focused on very high-level abstractions of robot capabilities and of the environment in which they operate. However, to be useful in a robotic context, the model provided to an AI planner should afford both symbolic and metric constructs; its expressiveness should not hinder computational efficiency; and it should include causal, spatial, temporal and resource aspects of the domain. We propose a planner grounded on well-founded constraint-based calculi that adhere to these requirements. A proof of completeness is provided, and the flexibility and portability of the approach is validated through several experiments on real and simulated robot platforms

AB - AI-based solutions for robot planning have so far focused on very high-level abstractions of robot capabilities and of the environment in which they operate. However, to be useful in a robotic context, the model provided to an AI planner should afford both symbolic and metric constructs; its expressiveness should not hinder computational efficiency; and it should include causal, spatial, temporal and resource aspects of the domain. We propose a planner grounded on well-founded constraint-based calculi that adhere to these requirements. A proof of completeness is provided, and the flexibility and portability of the approach is validated through several experiments on real and simulated robot platforms

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Y2 - 31 May 2014 through 7 June 2014

ER -

More knowledge on the table: Planning with space, time and resources for robots

Abstract

Conference

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