Design, Modeling, and Control of a Low-Cost and Rapid Response Soft-Growing Manipulator for Orchard Operations

Ryan Dorosh, Justin Allen, Zixuan He, Christopher Ninatanta, Jack Coleman, Jack Spieker, Ethan Tuck, Jordan Kurtz,Qin Zhang,Matthew D. Whiting, Jiecai Luo,Manoj Karkee,Ming Luo

2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, IROS(2023)

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摘要
Tree fruit growers around the world are facing labor shortages for critical operations, including harvest and pruning. There is a great interest in developing robotic solutions for these labor-intensive tasks, but current efforts have been prohibitively costly, slow, or require a reconfiguration of the orchard in order to function. In this paper, we introduce an alternative approach to robotics using a novel and low-cost soft-growing robotic platform. Our platform features the ability to extend up to 1.2 m linearly at a maximum speed of 0.27 m/s. The soft-growing robotic arm can operate with a terminal payload of up to 1.4 kg (4.4 N), more than sufficient for carrying an apple. This platform decouples linear and steering motions to simplify path planning and the controller design for targeting. We anticipate our platform being relatively simple to maintain compared to rigid robotic arms. Herein we also describe and experimentally verify the platform's kinematic model, including the prediction of the relationship between the steering angle and the angular positions of the three steering motors. Information from the model enables the position controller to guide the end effector to the targeted positions faster and with higher stability than without this information. Overall, our research show promise for using soft-growing robotic platforms in orchard operations.
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