Abstract Legged robots have a unique capability of traversing rough terrains and negotiating cluttered environments. Recent control development of legged robots has enabled robust locomotion on rough terrains. However, such approaches mainly focus on maintaining balance for the robot body. In this work, we are interested in leveraging the whole body of the robot to pass through a permeable obstacle (e.g., a small confined opening) with height, width, and terrain constraints. This paper presents a planning framework for legged robots manipulating their body and legs to perform collision-free locomotion through a permeable obstacle. The planner incorporates quadrupedal gait constraint, biasing scheme, and safety margin for the simultaneous body and foothold motion planning. We perform informed sampling for the body poses and swing foot position based on the gait constraint while ensuring stability and collision avoidance. The footholds are planned based on the terrain and the contact constraint. We also integrate the planner with robot control to execute the planned trajectory successfully. We validated our approach in high-fidelity simulation and hardware experiments on the Unitree A1 robot navigating through different representative permeable obstacles.
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Real‐Time Remodeling of Granular Terrain for Robot Locomotion
Terrain irregularities in natural environments present mobility challenges for autonomous robots and vehicles. Loosely consolidated sandy slopes flow unpredictably when perturbed, often leading to locomotion failure. Systematic experiments with various robot morphologies on flowable terrains feature open‐loop quasistatic gait strategies that remodel the terrain to aid locomotor kinematics. On a sloped terrain of granular media near the critical angle, a laboratory‐scale rover robot induces a flow via a localized fluidization gait to remodel local terrain and succeed in locomotion. A Bayesian optimization machine learning approach that modulates this gait strategy then finds a pattern of selectively fluidizing and solidifying terrain to climb slopes rapidly. In a biped walker robot, a cleated foot design dynamically manipulates the stress fields of flowable slopes. The deeply submerged cleats remodel the shear response of the material by creating jammed regions behind them which then improve forward progression by reducing slip when compared to a flat foot. The “robophysics” approach of systematic experiments exploring terrain reconfiguration combined with future machine learning models of flowable terrain evolution can augment gait discovery for future robots.
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- Award ID(s):
- 1806833
- NSF-PAR ID:
- 10459157
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 4
- Issue:
- 12
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/aisy.202200119
