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akaturk Akademik ölçüm

Makale detayı · 2011

Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation

OpenAlex Atıf 129 Üst %1 Yüzdelik 99.0% FWCI 10.86
Yıl
2011
Tür
conference-paper

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Özet

OpenAlex · İngilizce

We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The frame-work features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state/transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning/prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper.

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129 atıf

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Yerel katalogda bu makaleye atıf yapan 39 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).

  1. Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution 2012 Atıf 82 · OpenAlex
  2. Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution 2012 Atıf 82 · OpenAlex
  3. Geometric rearrangement of multiple movable objects on cluttered surfaces: A hybrid reasoning approach 2014 Atıf 81 · OpenAlex
  4. A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks 2016 Atıf 33 · OpenAlex
  5. A case study on the Tower of Hanoi challenge: Representation, reasoning and execution 2013 Atıf 33 · OpenAlex
  6. A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks 2016 Atıf 32 · OpenAlex
  7. Finding optimal feasible global plans for multiple teams of heterogeneous robots using hybrid reasoning: an application to cognitive factories 2019 Atıf 27 · OpenAlex
  8. Finding optimal feasible global plans for multiple teams of heterogeneous robots using hybrid reasoning: an application to cognitive factories 2019 Atıf 27 · OpenAlex
  9. Finding optimal feasible global plans for multiple teams of heterogeneous robots using hybrid reasoning: an application to cognitive factories 2018 Atıf 27 · OpenAlex
  10. REACT!: An Interactive Educational Tool for AI Planning for Robotics 2015 Atıf 27 · OpenAlex

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