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Carnegie Mellon Robotics Institute

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Abstract

Despite the stable walking capabilities of modern biped humanoid robots, their ability to autonomously and safely navigate obstacle-filled, unpredictable environments has so far been limited. We present an approach to autonomous humanoid walking that combines vision-based sensing with a footstep planner, allowing the robot to navigate toward a desired goal position while avoiding obstacles. An environment map including the robot, goal, and obstacle locations is built in real-time from vision. The footstep planner then computes an optimal sequence of footstep locations within a time-limited planning horizon. Footstep plans are reused and only partially recomputed as the environment changes during the walking sequence. In our experiments, combining real-time vision with plan reuse has allowed a Honda ASIMO humanoid robot to autonomously traverse dynamic environments containing unpredictably moving obstacles.

Keywords

Humanoid Robot, Vision, Footstep Planning, Obstacle Avoidance, Dynamic Replanning

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Planning and Autonomy Lab

Associated Project(s): Perception for Humanoid Robots and Footstep Planning for Biped Robots

Number of pages: 6

Text Reference

Philipp Michel, Joel Chestnutt, James Kuffner, and Takeo Kanade, "Vision-Guided Humanoid Footstep Planning for Dynamic Environments," Proceedings of the IEEE-RAS Conference on Humanoid Robots (Humanoids'05), December, 2005, pp. 13 - 18.

BibTeX Reference

@inproceedings{Michel_2005_5160,
   author = "Philipp Michel and Joel Chestnutt and James Kuffner and Takeo Kanade",
   title = "Vision-Guided Humanoid Footstep Planning for Dynamic Environments",
   booktitle = "Proceedings of the IEEE-RAS Conference on Humanoid Robots (Humanoids'05)",
   pages = "13 - 18",
   month = "December",
   year = "2005",
}

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