An everting inflatable fabric manipulator (EIFM) designed for apple picking

• 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. 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.

Authors

Institutions

Publication Details

Journal
Smart Agricultural Technology
Published
2025-11-17
DOI
https://doi.org/10.1016/j.atech.2025.101635
Primary Topic
Tree Root and Stability Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An everting inflatable fabric manipulator (EIFM) designed for apple picking

Manoj Karkee, Kyle T. Yoshida, J.M. Allen, Jiecai Luo et al.
Smart Agricultural Technology
Tree Root and Stability Studies
article

An everting inflatable fabric manipulator (EIFM) designed for apple picking

Manoj Karkee, Kyle T. Yoshida, J.M. Allen, Jiecai Luo, Ryan Dorosh, Christopher Ninatanta, Ming Luo, Matthew D. Whiting
article en

Abstract

• 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. 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.

Smart Agricultural TechnologyVol. 13
Southern University and Agricultural and Mechanical College (US), Cornell University (US), Washington State University (US)
Openalex Percentile: Top 64%
Tree Root and Stability Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.