Design, Modeling, and Redundancy Resolution of Soft Robot for Effective Harvesting

TL;DR

This paper introduces a soft robotic system with advanced control and visual feedback for efficient blackberry harvesting, demonstrating high accuracy and robustness in both lab and field tests.

Contribution

It presents a novel, modular soft robotic arm with a real-time redundancy resolution algorithm using visual feedback, optimized for practical harvesting applications.

Findings

Achieved position tracking error of 0.64 mm in benchtop tests

Reaching accuracy of 0.75 mm during fine positioning

Validated hardware and control system in field conditions

Abstract

Blackberry harvesting is a labor-intensive and costly process, consuming up to 50\% of the total annual crop hours. This paper presents a solution for robotic harvesting through the design, manufacturing, integration, and control of a pneumatically actuated, kinematically redundant soft arm with a tendon-driven soft robotic gripper. The hardware design is optimized for durability and modularity for practical use. The harvesting process is divided into four stages: initial placement, fine positioning, grasp, and move back to home position. For initial placement, we propose a real-time, continuous gain-scheduled redundancy resolution algorithm for simultaneous position and orientation control with joint-limit avoidance. The algorithm relies solely on visual feedback from an eye-to-hand camera and achieved a position and orientation tracking error of $0.64\pm{0.27}$ mm and $1.08\pm{1.5}^{\circ}$, respectively, in benchtop settings. Following accurate initial placement of the robotic arm, fine positioning is achieved using a combination of eye-in-hand and eye-to-hand visual feedback, reaching an accuracy of $0.75\pm{0.36}$ mm. The system's hardware, feedback framework, and control methods are thoroughly validated through benchtop and field tests, confirming feasibility for practical applications.