用混合夹爪和视觉检测实现番茄自动采摘，成功率80%
A Novel Approach to Tomato Harvesting Using a Hybrid Gripper with Semantic Segmentation and Keypoint Detection
- 软硬结合夹爪+视觉定位，精准抓取成熟番茄
- 平均单次采摘24.34秒，抓力仅0.20–0.50牛，不伤果
- 适合复杂环境下的自动化农业采摘，可推广至其他果实
本文提出一种基于混合机器人夹爪的自主番茄采摘系统，夹爪由六个柔性辅助结构手指与刚性外骨骼及乳胶篮组成，实现类似笼状的轻柔抓握。通过伺服驱动的滑块连杆机构驱动，配备分离叶片形成锥台结构以隔离果实，并集成微型伺服切割器完成果柄切断。感知方面，采用RGB-D相机与基于Detectron2的处理流程，在遮挡和光照变化条件下完成成熟/未熟番茄的语义分割及果柄、果实中心的关键点定位。基于虚功原理建立解析模型，关联伺服扭矩与抓握力，支持驱动设计层面的分析。执行阶段，通过安装在部分手指上的力敏电阻反馈，采用比例-积分-微分（PID）控制器实现闭环抓握力调节，防止滑脱与损伤。运动规划采用粒子群优化（PSO）方法，针对五自由度机械臂生成轨迹。实验验证了从接近、分离、切割、抓取、运输到释放的完整采摘流程，平均周期为24.34秒，整体成功率约80%，抓握力保持在0.20–0.50牛之间。结果表明，该混合夹爪与集成视觉-控制流程可在杂乱环境中实现可靠采摘。
原文摘要 · Abstract (English)
This paper presents an autonomous tomato-harvesting system built around a hybrid robotic gripper that combines six soft auxetic fingers with a rigid exoskeleton and a latex basket to achieve gentle, cage-like grasping. The gripper is driven by a servo-actuated Scotch--yoke mechanism, and includes separator leaves that form a conical frustum for fruit isolation, with an integrated micro-servo cutter for pedicel cutting. For perception, an RGB--D camera and a Detectron2-based pipeline perform semantic segmentation of ripe/unripe tomatoes and keypoint localization of the pedicel and fruit center under occlusion and variable illumination. An analytical model derived using the principle of virtual work relates servo torque to grasp force, enabling design-level reasoning about actuation requirements. During execution, closed-loop grasp-force regulation is achieved using a proportional--integral--derivative controller with feedback from force-sensitive resistors mounted on selected fingers to prevent slip and bruising. Motion execution is supported by Particle Swarm Optimization (PSO)--based trajectory planning for a 5-DOF manipulator. Experiments demonstrate complete picking cycles (approach, separation, cutting, grasping, transport, release) with an average cycle time of 24.34~s and an overall success rate of approximately 80\%, while maintaining low grasp forces (0.20--0.50~N). These results validate the proposed hybrid gripper and integrated vision--control pipeline for reliable harvesting in cluttered environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。