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Related Experiment Videos

Posture-based motion planning: applications to grasping.

D A Rosenbaum1, R J Meulenbroek, J Vaughan

  • 1Department of Psychology, Pennsylvania State University, 642 Moore Building, University Park, Pennsylvania 16802, USA. dar12@psu.edu

Psychological Review
|November 9, 2001
PubMed
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This study presents a motion planning model for grasping, emphasizing a constraint hierarchy to prioritize task requirements like collision avoidance and effort minimization. The model simulates flexible prehension by combining stored and generated movements, effectively accounting for diverse data.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Motion planning is crucial for robotic grasping.
  • Existing models often lack flexibility in handling complex grasping scenarios.
  • A hierarchical approach to constraints can improve planning efficiency.

Purpose of the Study:

  • To introduce a novel motion planning model specifically for grasping.
  • To demonstrate the importance of a constraint hierarchy in grasping tasks.
  • To simulate flexible and adaptable prehension.

Main Methods:

  • Developing a constraint hierarchy prioritizing collision avoidance and movement effort.
  • Integrating instance retrieval (recalling stored postures) and instance generation (creating new postures).

Related Experiment Videos

  • Employing dynamic deadline setting for regulating instance generation and multi-movement execution for obstacle avoidance.

    • Main Results:

    • The model successfully simulates flexible prehension by combining stored and generated movements.
    • Constraint hierarchy effectively guides the planning process for grasping.
    • Dynamic deadlines and multi-movement execution enhance adaptability and obstacle avoidance.

    Conclusions:

    • The proposed motion planning model provides a robust framework for grasping.
    • Constraint hierarchies are fundamental for achieving adaptable and efficient robotic prehension.
    • The model accounts for both previously observed and novel grasping data.