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.

翻译：采摘黑莓是一项劳动密集型和成本高昂的过程，消耗了总年度作物工时的高达50％。本论文通过设计、制造、集成和控制气动臂和张力驱动的软式机器人夹爪的运动冗余，提出了一种机器人采摘的解决方案。硬件设计优化了实用性的耐久性和模块化。采摘过程分为四个阶段：初始放置、精确定位、抓取和返回原位。对于初始放置，我们提出了一个实时的、连续的增益调度冗余解析算法，用于关节限制避免的同时位置和方向控制。该算法仅依赖于从目视反馈的眼到手相机，可以在台式机设置中实现$0.64\pm{0.27}$毫米的位置跟踪误差和$1.08\pm{1.5}^{\circ}$的方向跟踪误差。在准确放置机器人臂之后，使用眼内和眼外视觉反馈的组合实现了精确定位，达到了$0.75\pm{0.36}$毫米的精度。本系统的硬件、反馈框架和控制方法在台式机和现场测试中得到了全面验证，证实了实际应用的可行性。