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Cloud-Based Grasp Analysis and Planning for Toleranced Parts Using Parallelized Monte Carlo Sampling

  • Author(s): Kehoe, B
  • Warrier, D
  • Patil, S
  • Goldberg, K
  • et al.

This paper considers grasp planning in the presence of shape uncertainty and explores how cloud computing can facilitate parallel Monte Carlo sampling of combination actions and shape perturbations to estimate a lower bound on the probability of achieving force closure. We focus on parallel-jaw push grasping for the class of parts that can be modeled as extruded 2-D polygons with statistical tolerancing. We describe an extension to model part slip and experimental results with an adaptive sampling algorithm that can reduce sample size by 90%. We show how the algorithm can also bound part tolerance for a given grasp quality level and report a sensitivity analysis on algorithm parameters. We test a cloud-based implementation with varying numbers of nodes, obtaining a 515x speedup with 500 nodes in one case, suggesting the algorithm can scale linearly when all nodes are reliable. Code and data are available at:

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