Rough Terrain Navigation for Legged Robots using Reachability Planning and Template Learning

Open access
Date
2021Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
Abstract
Navigation planning for legged robots has distinct
challenges compared to wheeled and tracked systems due to
the ability to lift legs off the ground and step over obstacles.
While most navigation planners assume a fixed traversability
value for a single terrain patch, we overcome this limitation by
proposing a reachability-based navigation planner for legged
robots. We approximate the robot morphology by a set of
reachability and body volumes, assuming that the reachability
volumes need to always be in contact with the environment,
while the body should be contact-free. We train a convolutional
neural network to predict foothold scores which are used to
restrict geometries which are considered suitable to step on.
Using this representation, we propose a navigation planner
based on probabilistic roadmaps. Through validation of only
low-cost graph edges during graph expansion and an adaptive
sampling scheme based on roadmap node density, we achieve
real-time performance with fast update rates even in cluttered
and narrow environments. We thoroughly validate the proposed
navigation planner in simulation and demonstrate its performance in real-world experiments on the quadruped ANYmal. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000507668Publication status
publishedExternal links
Book title
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Pages / Article No.
Publisher
IEEEEvent
Subject
Navigation; RoboticsOrganisational unit
09570 - Hutter, Marco / Hutter, Marco
Funding
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)
780883 - subTerranean Haptic INvestiGator (EC)
852044 - Learning Mobility for Real Legged Robots (EC)
101016970 - Natural Intelligence for Robotic Monitoring of Habitats (EC)
Related publications and datasets
Is continued by: https://doi.org/10.3929/ethz-b-000614683
More
Show all metadata
ETH Bibliography
yes
Altmetrics