Incremental Micro-UAV Motion Replanning for Exploring Unknown Environments

Mihail Pivtoraiko, Daniel Mellinger, and Vijay Kumar. Incremental Micro-UAV Motion Replanning for Exploring Unknown Environments. In Proceedings of the IEEE International Conference on Robotics and Automation, 2013.

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Abstract

This paper describes an approach to motion gen- eration for quadrotor micro-UAV’s navigating cluttered and partially known environments. We pursue a graph search method that, despite the high dimensionality of the problem, the complex dynamics of the system and the continuously changing environment model is capable of generating dynamically feasible motions in real-time. This is enabled by leveraging the differential flatness property of the system and by developing a structured search space based on state lattice motion primitives. We suggest a greedy algorithm to generate these primitives off-line automatically, given the robot’s motion model. The process samples the reachability of the system and reduces it to a set of representative, canonical motions that are compatible with the state lattice structure, which guarantees that any incremental replanning algorithm is able to produce smooth dynamically feasible motion plans while reusing previous computation between replans. Simulated and physical experimental results demonstrate real-time replanning due to the inevitable and frequent world model updates during micro-UAV motion in partially known environments.

BibTeX

@INPROCEEDINGS{pivtoraiko_mellinger_kumar_icra13,
  author = {Mihail Pivtoraiko and Daniel Mellinger and Vijay Kumar},
  title = {Incremental Micro-UAV Motion Replanning for Exploring Unknown Environments},
  booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation},
  year = {2013},
  abstract = {This paper describes an approach to motion gen- eration
                  for quadrotor micro-UAV’s navigating cluttered and
                  partially known environments. We pursue a graph
                  search method that, despite the high dimensionality
                  of the problem, the complex dynamics of the system
                  and the continuously changing environment model is
                  capable of generating dynamically feasible motions
                  in real-time. This is enabled by leveraging the
                  differential flatness property of the system and by
                  developing a structured search space based on state
                  lattice motion primitives. We suggest a greedy
                  algorithm to generate these primitives off-line
                  automatically, given the robot’s motion model. The
                  process samples the reachability of the system and
                  reduces it to a set of representative, canonical
                  motions that are compatible with the state lattice
                  structure, which guarantees that any incremental
                  replanning algorithm is able to produce smooth
                  dynamically feasible motion plans while reusing
                  previous computation between replans. Simulated and
                  physical experimental results demonstrate real-time
                  replanning due to the inevitable and frequent world
                  model updates during micro-UAV motion in partially
                  known environments.},
  bib2html_pubtype = {Refereed Conference Papers},
  bib2html_rescat = {Kinodynamic Planning}
}

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