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Decoupling Constraints from Sampling-Based Planners

  • Zachary KingstonEmail author
  • Mark Moll
  • Lydia E. Kavraki
Conference paper
  • 198 Downloads
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

We present a general unifying framework for sampling-based motion planning under kinematic task constraints which enables a broad class of planners to compute plans that satisfy a given constraint function that encodes, e.g., loop closure, balance, and end-effector constraints. The framework decouples a planner’s method for exploration from constraint satisfaction by representing the implicit configuration space defined by a constraint function. We emulate three constraint satisfaction methodologies from the literature, and demonstrate the framework with a range of planners utilizing these constraint methodologies. Our results show that the appropriate choice of constrained satisfaction methodology depends on many factors, e.g., the dimension of the configuration space and implicit constraint manifold, and number of obstacles. Furthermore, we show that novel combinations of planners and constraint satisfaction methodologies can be more effective than previous approaches. The framework is also easily extended for novel planners and constraint spaces.

Keywords

Sampling-based motion planning Constrained motion planning 

Notes

Acknowledgements

We would like to thank Caleb Voss for his preliminary work [42]. ZK, MM, and LEK are supported by NSF IIS 1317849 and Rice University funds. ZK is also supported by a NASA Space Technology Research Fellowship 80NSSC17K0163.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zachary Kingston
    • 1
    Email author
  • Mark Moll
    • 1
  • Lydia E. Kavraki
    • 1
  1. 1.Rice UniversityHoustonUSA

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