An Open-Source, Reproducible Tensegrity Robot that can Navigate Among Obstacles

TL;DR

This paper introduces an open-source, reproducible navigation system for a tensegrity robot, enabling obstacle avoidance and path planning in complex terrains, validated through outdoor and multi-lab experiments.

Contribution

It provides the first complete, open-source hardware and software system for tensegrity robot navigation, facilitating research and development in compliant, impact-resistant robots.

Findings

Successfully navigated complex terrains including drops and inclines

Demonstrated robustness in unmodeled environmental challenges

Validated reproducibility across different laboratories

Abstract

Tensegrity robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, coupled dynamics, however, present modeling and control challenges, hindering path planning and obstacle avoidance. This paper presents a complete, open-source, and reproducible system that enables navigation for a 3-bar tensegrity robot. The system comprises: (i) an inexpensive, open-source hardware design, and (ii) an integrated, open-source software stack for physics-based modeling, system identification, state estimation, path planning, and control. All hardware and software are publicly available at https://sites.google.com/view/tensegrity-navigation/. The proposed system tracks the robot's pose and executes collision-free paths to a specified goal among known obstacle locations. System robustness is demonstrated through experiments involving unmodeled environmental challenges, including a vertical drop, an incline, and granular media, culminating in an outdoor field demonstration. To validate reproducibility, experiments were conducted using robot instances at two different laboratories. This work provides the robotics community with a complete navigation system for a compliant, impact-resistant, and shape-morphing robot. This system is intended to serve as a springboard for advancing the navigation capabilities of other unconventional robotic platforms.