ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots

This article focuses on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot...

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Bibliographic Details
Published in:IEEE transactions on robotics Vol. 39; no. 2; pp. 1 - 20
Main Authors: Fahmi, Shamel, Barasuol, Victor, Esteban, Domingo, Villarreal, Octavio, Semini, Claudio
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation
Algorithms
Foot
Forward error correction
Legged locomotion
Legged robots
Locomotion
Optimization
optimization and optimal control
Planning
Robot dynamics
Robots
Strategy
Terrain
Trajectory
visual learning
whole-body motion planning and control
ISSN:1552-3098, 1941-0468
Online Access:Get full text
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Summary:This article focuses on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a vision-based terrain-aware locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative to given footholds, but the body pose that maximizes the chances of the legs in reaching safe footholds. ViTAL plans footholds and poses based on skills that characterize the robot's capabilities and its terrain awareness. We use the 90-kg HyQ and 140-kg HyQReal quadruped robots to validate ViTAL and show that they are able to climb various obstacles, including stairs, gaps, and rough terrains, at different speeds and gaits. We compare ViTAL with a baseline strategy that selects the robot pose based on given selected footholds and show that ViTAL outperforms the baseline.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2022.3222958