Published April 19, 2025 | Published
Journal Article Open

ATMO: an aerially transforming morphobot for dynamic ground-aerial transition

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Northeastern University

Abstract

Designing ground-aerial robots is challenging due to the increased actuation requirements which can lead to added weight and reduced locomotion efficiency. Morphobots mitigate this by combining actuators into multi-functional groups and leveraging ground transformation to achieve different locomotion modes. However, transforming on the ground requires dealing with the complexity of ground-vehicle interactions during morphing, limiting applicability on rough terrain. Mid-air transformation offers a solution to this issue but demands operating near or beyond actuator limits while managing complex aerodynamic forces. We address this problem by introducing the Aerially Transforming Morphobot (ATMO), a robot which transforms near the ground achieving smooth transition between aerial and ground modes. To achieve this, we leverage the near ground aerodynamics, uncovered by experimental load cell testing, and stabilize the system using a model-predictive controller that adapts to ground proximity and body shape. The system is validated through numerous experimental demonstrations. We find that ATMO can land smoothly at body postures past its actuator saturation limits by virtue of the uncovered ground-effect.

Copyright and License

© 2025, The Author(s). This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Acknowledgement

We are grateful to Gabriel Margaria, Quentin Delfosse, Vincent Gherold, Simon Gmür and Alejandro Stefan-Zavala who helped with flight experiments and Scott Bollt and Julian Humml who provided their assistance in setting up the lasers for smoke visualization and the robotic arm used for load cell testing. We also thank Alexandros Rosakis for comments on an early draft of the manuscript. This project was supported by funding from the Center for Autonomous Systems and Technologies at Caltech. Ioannis Mandralis is an Onassis Scholar and was supported by the GALCIT endowment graduate student fellowship.

Code Availability

The code developed for this work is available at: https://github.com/mandralis/ATMO.

Supplemental Material

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Additional details

Created:
April 23, 2025
Modified:
April 23, 2025