HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes accepted to IEEE RA-L

Yiming Yang, Wolfgang Merkt, Vladimir Ivan, Zhibin Li, and Sethu Vijayakumar. “HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes”. IEEE Robotics and Automation Letters, 2018, In Press.

Publisher’s link – DOI: 10.1109/LRA.2017.2773669

Abstract

We present the Hierarchical Dynamic Roadmap (HDRM), a novel resolution complete motion planning algorithm for solving complex planning problems. A unique hierarchical structure is proposed for efficiently encoding the configuration-to-workspace occupation information that allows the robot to check the collision state of tens of millions of samples on-the-fly—the number of which was previously strictly limited by available memory. The hierarchical structure also significantly reduces the time for path searching, hence the robot is able to find feasible motion plans in real-time in extremely constrained environments. The HDRM is theoretically proven to be resolution complete, with a rigorous benchmarking showing that HDRM is robust and computationally fast, compared to classical dynamic roadmap methods and other state-of-the-art planning algorithms. Experiments on the 7 degree-of-freedom KUKA LWR robotic arm integrated with real-time perception of the environment further validate the effectiveness of HDRM in complex environments.

Bibtex

@ARTICLE{yang2018hdrm,
author={Y. Yang and W. Merkt and V. Ivan and Z. Li and S. Vijayakumar},
journal={IEEE Robotics and Automation Letters},
title={HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes},
year={2018},
volume={3},
number={1},
pages={551-558},
keywords={Collision avoidance;Dynamics;Heuristic algorithms;Planning;Probabilistic logic;Real-time systems;Robots;Motion planning;collision avoidance;dynamic roadmap;realtime planning},
doi={10.1109/LRA.2017.2773669},
ISSN={},
month={Jan},}