Overconstrained Locomotion

Fuente: arXiv
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Autori principali: Sun, Haoran, Huang, Bangchao, Zhang, Zishang, Xu, Ronghan, Huang, Guojing, Feng, Shihao, Huang, Guangyi, Yin, Jiayi, Qiu, Nuofan, Chen, Hua, Zhang, Wei, Pan, Jia, Wan, Fang, Song, Chaoyang
Natura: Preprint
Pubblicazione: 2023
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author Sun, Haoran
Huang, Bangchao
Zhang, Zishang
Xu, Ronghan
Huang, Guojing
Feng, Shihao
Huang, Guangyi
Yin, Jiayi
Qiu, Nuofan
Chen, Hua
Zhang, Wei
Pan, Jia
Wan, Fang
Song, Chaoyang
author_facet Sun, Haoran
Huang, Bangchao
Zhang, Zishang
Xu, Ronghan
Huang, Guojing
Feng, Shihao
Huang, Guangyi
Yin, Jiayi
Qiu, Nuofan
Chen, Hua
Zhang, Wei
Pan, Jia
Wan, Fang
Song, Chaoyang
contents This paper studies the design, control, and learning of a novel robotic limb that produces overconstrained locomotion by employing the Bennett linkage for motion generation, capable of parametric reconfiguration between a reptile- and mammal-inspired morphology within a single quadruped. In contrast to the prevailing focus on planar linkages, this research delves into adopting overconstrained linkages as the limb mechanism. The overconstrained linkages have solid theoretical foundations in advanced kinematics but are under-explored in robotic applications. This study showcases the morphological superiority of Overconstrained Robotic Limbs (ORLs) that can transform into planar or spherical limbs, exemplified using the simplest case of a Bennett linkage as an ORL. We apply Model Predictive Control (MPC) to simulate a range of overconstrained locomotion tasks, revealing its superiority in energy efficiency against planar limbs when considering foothold distances and speeds. The results are further verified in overconstrained locomotion policies optimized from Reinforcement Learning (RL). From an evolutionary biology perspective, these findings highlight the mechanism distinctions in limb design between reptiles and mammals and represent the first documented instance of ORLs outperforming planar limb designs in dynamic locomotion. Future studies will focus on deploying the model-based and learning-based overconstrained locomotion skills in the robotic hardware to close the Sim2Real gap for developing evolutionary-inspired, energy-efficient control of novel robotic limbs.
format Preprint
id arxiv_https___arxiv_org_abs_2310_09824
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Overconstrained Locomotion
Sun, Haoran
Huang, Bangchao
Zhang, Zishang
Xu, Ronghan
Huang, Guojing
Feng, Shihao
Huang, Guangyi
Yin, Jiayi
Qiu, Nuofan
Chen, Hua
Zhang, Wei
Pan, Jia
Wan, Fang
Song, Chaoyang
Robotics
This paper studies the design, control, and learning of a novel robotic limb that produces overconstrained locomotion by employing the Bennett linkage for motion generation, capable of parametric reconfiguration between a reptile- and mammal-inspired morphology within a single quadruped. In contrast to the prevailing focus on planar linkages, this research delves into adopting overconstrained linkages as the limb mechanism. The overconstrained linkages have solid theoretical foundations in advanced kinematics but are under-explored in robotic applications. This study showcases the morphological superiority of Overconstrained Robotic Limbs (ORLs) that can transform into planar or spherical limbs, exemplified using the simplest case of a Bennett linkage as an ORL. We apply Model Predictive Control (MPC) to simulate a range of overconstrained locomotion tasks, revealing its superiority in energy efficiency against planar limbs when considering foothold distances and speeds. The results are further verified in overconstrained locomotion policies optimized from Reinforcement Learning (RL). From an evolutionary biology perspective, these findings highlight the mechanism distinctions in limb design between reptiles and mammals and represent the first documented instance of ORLs outperforming planar limb designs in dynamic locomotion. Future studies will focus on deploying the model-based and learning-based overconstrained locomotion skills in the robotic hardware to close the Sim2Real gap for developing evolutionary-inspired, energy-efficient control of novel robotic limbs.
title Overconstrained Locomotion
topic Robotics
url https://arxiv.org/abs/2310.09824