1
Return

Impact-resistant, autonomous robots inspired by tensegrity architecture

delete2026-08-10
delete0
PRE
AI
W
William R. Johnson
X
Xiaonan Huang
S
Shiyang Lu
K
Kun Wang
L
Luca Cimatti
M
Marco Carati
J
Joran Booth
K
Kostas E. Bekris
R
Rebecca Kramer‐Bottiglio *
DOI:10.1038/s42256-026-01280-2delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Tensegrity robots—composed of rigid struts suspended in a network of elastic cables—have been proposed as the next generation of planetary rovers and disaster response platforms, with their inherent impact resilience potentially eliminating the need for separate landers if they can survive aerial deployment. Although non-robotic tensegrity structures have demonstrated substantial impact resistance, integrating this property into autonomous robotic systems remains a grand challenge. Here we present Tribar, a three-bar tensegrity robot capable of surviving high-impact landings (at least 5.7 m) and autonomously navigating unstructured terrain post impact. We characterize the robot’s locomotion, evaluate its autonomous navigation capabilities, benchmark its performance relative to state-of-the-art tensegrity robots and demonstrate its robustness through successful locomotion after a cliff fall. Johnson et al. demonstrate an autonomous three-bar tensegrity robot capable of robust locomotion across varied terrains even after extreme impacts, including a 5.7-m drop onto asphalt.

Journal

Nature Machine Intelligence cover
Nature Machine Intelligence
IF:
23.9
Papers:
1.3K
Citations:
1.5W

Organization

R
rutgers university
Scholars:
1.2K
Papers: 696
Citations: 0
Y
yale university
Scholars:
7.0K
Papers: 3.0K
Citations: 2
U
university of bologna
Scholars:
4.9K
Papers: 2.1K
Citations: 0
Cited Papers

Cited Papers

Citing Papers

Citing Papers