Agile Plane Transition of a Hexapod Climbing Robot

Traversing across adjacent planes is an important ability for legged climbing robots. While many robots can achieve autonomous ground-to-wall transitions, most are limited to scenarios where the angle between the planes has a certain value. In some cases, however, the robot needs to traverse planes with a wide variety of angles. To enhance the adaptability of the robot in such diverse scenarios, we analyze the plane transition process and propose a universal methodology for hexapod climbing robots with a two-stage workflow. In the first stage, we plan a trajectory of body without considering configuration of legs, within a reachable map. This low-dimensional map can be efficiently sampled and explored to identify feasible transitions. In the second stage, we use a motion prediction to generate landing points, as well as swing and stance trajectories for each leg. By tracking these trajectories, the robot can autonomously transition from one plane to another. Guided by this methodology, we design a hexapod climbing robot capable of autonomously traversing planes with angles ranging from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30^{\circ }$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$270^{\circ }$</tex-math></inline-formula>. For further validation, we build the physical prototype of the robot and conduct a series of plane transition experiments. The results demonstrate the feasibility of both our methodology and the robot.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-04-14
DOI
https://doi.org/10.1109/lra.2025.3560894
Citations
1
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.71
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article

Agile Plane Transition of a Hexapod Climbing Robot

Chao Xu, Dan Wang, Chengzhang Gong, Li Fan
1 citations
IEEE Robotics and Automation Letters
Modular Robots and Swarm Intelligence
0.71
article

Agile Plane Transition of a Hexapod Climbing Robot

Chao Xu, Dan Wang, Chengzhang Gong, Li Fan
article en
1 citations

Abstract

Traversing across adjacent planes is an important ability for legged climbing robots. While many robots can achieve autonomous ground-to-wall transitions, most are limited to scenarios where the angle between the planes has a certain value. In some cases, however, the robot needs to traverse planes with a wide variety of angles. To enhance the adaptability of the robot in such diverse scenarios, we analyze the plane transition process and propose a universal methodology for hexapod climbing robots with a two-stage workflow. In the first stage, we plan a trajectory of body without considering configuration of legs, within a reachable map. This low-dimensional map can be efficiently sampled and explored to identify feasible transitions. In the second stage, we use a motion prediction to generate landing points, as well as swing and stance trajectories for each leg. By tracking these trajectories, the robot can autonomously transition from one plane to another. Guided by this methodology, we design a hexapod climbing robot capable of autonomously traversing planes with angles ranging from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30^{\circ }$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$270^{\circ }$</tex-math></inline-formula>. For further validation, we build the physical prototype of the robot and conduct a series of plane transition experiments. The results demonstrate the feasibility of both our methodology and the robot.

IEEE Robotics and Automation LettersVol. 10(6)
Huzhou Normal University (CN), Zhejiang University (CN)
Openalex Percentile: Top 34%
Modular Robots and Swarm Intelligence
0.71
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