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Geometric backtracking for combined task and motion planning in robotic systems
(AASS)
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0002-0458-2146
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0002-8631-7863
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0001-8229-1363
2017 (engelsk)Inngår i: Artificial Intelligence, ISSN 0004-3702, E-ISSN 1872-7921, Vol. 247, s. 229-265Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Planners for real robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse.

sted, utgiver, år, opplag, sider
Elsevier, 2017. Vol. 247, s. 229-265
Emneord [en]
Combined task and motion planning; Task planning; Action planning; Path planning; Robotics; Geometric reasoning; Hybrid reasoning; Robot manipulation
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Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-48015DOI: 10.1016/j.artint.2015.03.005ISI: 000401401600011Scopus ID: 2-s2.0-84929590433OAI: oai:DiVA.org:oru-48015DiVA, id: diva2:901046
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EU, FP7, Seventh Framework Programme, 248273Knowledge FoundationTilgjengelig fra: 2016-02-05 Laget: 2016-02-05 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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