@InProceedings{orthey:iros:2018,
  author    = {Orthey, Andreas and Escande, Adrien and Yoshida, Eiichi},
  title     = {Quotient-Space Motion Planning},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year      = {2018},
  pages     = {8089--8096},
  address   = {Madrid,Spain},
  month     = {October 1-October 5},
  url       = {https://staff.aist.go.jp/e.yoshida/papers/Orthey-IROS2018.pdf},
  keywords  = {Planning, Manupulators, Runtime, Visualization, Probabilistic logic, Manifolds},
  doi       = {https://doi.org/10.1109/IROS.2018.8593554},
  abstract  = {Amotionplanningalgorithmcomputesthemotion of a robot by computing a path through its configuration space. To improve the runtime of motion planning algorithms, we propose to nest robots in each other, creating a nested quotientspace decomposition of the configuration space. Based on this decomposition we define a new roadmap-based motion planning algorithm called the Quotient-space roadMap Planner (QMP). Thealgorithmstartsgrowingagraphonthelowestdimensional quotient space, switches to the next quotient space once a valid path has been found, and keeps updating the graphs on each quotient space simultaneously until a valid path in the configuration space has been found. We show that this algorithm is probabilistically complete and outperforms a set of state-of-the-art algorithms implemented in the open motion planning library (OMPL).}
}