Tendon-Driven Soft Robotic Gripper for Blackberry Harvesting
Anthony L. Gunderman, Jeremy Collins, Andrea L. Myers, Renee T. Threlfall, Yue Chen
- Year
- 2022
- Citations
- 100
Abstract
Global berry production and consumption have significantly increased in recent years, coinciding with increased consumer awareness of the health-promoting benefits of berries. Of those consumed, fresh-market blackberries are primarily harvested by hand to maintain postharvest quality. However, the forces applied during hand harvesting can result in major losses of marketable berries to red drupelet reversion (RDR). What is more, manual harvesting is a costly endeavor that accounts for up to 50% of the person-hours involved in berry production. Herein, we present a novel, tendon-driven soft robotic gripper with active contact force feedback control, which leverages the passive compliance of the gripper to allow the gentle harvesting of berries. The versatile gripper can generate a desired force as low as 0.5 N with a mean error of 0.046 N. Field test results indicate that the robotic gripper is capable of harvesting berries with 16% RDR while maintaining a harvesting reliability of 95.24% at a harvesting rate of approximately 4.8 seconds per berry.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002