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：黑莓采摘是一项劳动密集且成本高昂的过程，其耗时占全年作物总工时的比例高达50%。本文提出了一种通过气动驱动、运动学冗余软体臂与腱驱动软体夹爪的设计、制造、集成与控制实现机器人化采摘的解决方案。硬件设计针对耐用性和模块化进行了优化，以适用于实际应用。采摘过程分为四个阶段：初始定位、精确定位、抓取及返回初始位置。在初始定位阶段，我们提出了一种实时、连续增益调度的冗余度求解算法，用于同时实现位置与姿态控制并避免关节限位。该算法仅依赖眼手分离视觉反馈，在实验台设置下实现了$0.64\pm{0.27}$毫米的位置跟踪误差和$1.08\pm{1.5}^{\circ}$的姿态跟踪误差。在机器人臂完成精确初始定位后，结合眼在手上与眼手分离视觉反馈实现了精确定位，精度达到$0.75\pm{0.36}$毫米。通过实验台测试与现场试验，系统的硬件、反馈框架及控制方法得到了全面验证，证实了其在实际应用中的可行性。