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Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs

Magaña, OAV; Barasuol, V; Camurri, M; Franceschi, L; Focchi, M; Pontil, M; Caldwell, DG; (2019) Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs. IEEE Robotics and Automation Letters , 4 (2) pp. 2140-2147. 10.1109/LRA.2019.2899434. Green open access

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Abstract

Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees remains a challenge. We present here a real-time, dynamic foothold adaptation strategy based on visual feedback. Our method adjusts the landing position of the feet in a fully reactive manner, using only on-board computers and sensors. The correction is computed and executed continuously along the swing phase trajectory of each leg. To efficiently adapt the landing position, we implement a self-supervised foothold classifier based on a convolutional neural network. Our method results in an up to 200 times faster computation with respect to the full-blown heuristics. Our goal is to react to visual stimuli from the environment, bridging the gap between blind reactive locomotion and purely vision-based planning strategies. We assess the performance of our method on the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe foothold adaptation is clearly demonstrated by the overall robot behavior.

Type: Article
Title: Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/LRA.2019.2899434
Publisher version: https://doi.org/10.1109/LRA.2019.2899434
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Legged locomotion, Robot sensing systems, Foot, Visualization, Trajectory, Reactive and Sensor-Based Planning, Deep Learning in Robotics and Automation
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10071573
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