2026-01-07 ワシントン州立大学（WSU）
＜関連情報＞
- https://news.wsu.edu/press-release/2026/01/07/inflatable-fabric-robotic-arm-picks-apples/
- https://www.sciencedirect.com/science/article/pii/S2772375525008664
リンゴ狩り用に設計された反転式インフレータブルファブリックマニピュレーター（EIFM） An everting inflatable fabric manipulator (EIFM) designed for apple picking
Ryan Dorosh, Justin Allen, Christopher Ninatanta, Matthew D. Whiting, Jiecai Luo, Kyle Yoshida, Manoj Karkee, Ming Luo
Smart Agricultural Technology Available online: 13 November 2025
DOI:https://doi.org/10.1016/j.atech.2025.101635
Graphical abstract
Highlights
- Self-supported everting vine robot capable of precise control with payloads.
- Low-complexity and low-cost design with robust control.
- Low mass and inertia reduce risk to trees, fruit, and workers.
- System designed for and adaptable to the modern orchard environment.
Abstract
Tree fruit growers worldwide are facing labor shortages for critical operations like harvesting and pruning. There is, therefore, great interest in labor-saving technologies, including robotics. Here we introduce the design and control of our everting inflatable fabric manipulator (EIFM) platform for apple harvesting. This system overhauls an everting vine robot to meet the unique challenges in the orchard environment and to conduct a practical industrial application. Our platform features a 0.75 m fabric arm that can extend and retract at 0.38 m/s and 0.26 m/s, respectively. At full extension, the EIFM can support a 10.6 N payload, sufficient for carrying an end-effector and an apple. We also implemented a model reference adaptive controller to ensure stability and consistent dynamics for the system with minimal complexity. Using this controller, the system can reach any point in its spherical sector-shaped workspace with varying payloads. The uncomplicated nature of the design makes it low-cost, easy to maintain, and highly reliable for a soft robot. The soft and low-inertia design also makes it safe for branches and fruit. Herein, we also experimentally verify the kinematic model and compare multiple control methods. Overall, our research demonstrates the robustness of an EIFM platform for future use in apple harvesting.
