| 王睿,陈挺木,曾辉雄,吴雨欣,高银.3D视觉引导无标定机械臂人机协同控制系统[J].电子测量与仪器学报,2026,40(3):93-105 |
| 3D视觉引导无标定机械臂人机协同控制系统 |
| 3D vision-guided calibration-free robotic arm human-machinecollaboration control system |
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| DOI： |
| 中文关键词: 机械臂控制 3D视觉感知 逆解运动学 无标定映射 人机协同 |
| 英文关键词:robotic arm control 3D visual perception inverse solution kinematics calibration-free mapping human-machine collaboration |
| 基金项目: |
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| Author | Institution |
| Wang Rui | School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China |
| Chen Tingmu | Fujian Special Equipment Inspection and Research Institute, Quanzhou 362000, China |
| Zeng Huixiong | Haixi Research Institute,
Chinese Academy of Sciences, Quanzhou 362216, China |
| Wu Yuxing | School of Intelligent Manufacturing and Control Engineering,
Shanghai Polytechnic University, Shanghai 201209, China |
| Gao Yin | Fujian Institute of Research on the Structure of Matter,
Chinese Academy of Sciences, Fuzhou 350025, China |
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| 中文摘要: |
| 鉴于工业机械臂控制系统面临人机协同精度欠佳、动态响应迟缓以及安全约束匮乏这三项核心难题,构思出一种依托于3D视觉引导的无标定机械臂协同控制系统。此系统别出心裁地整合轻量化深度学习感知与实时逆解运动学控制:于感知层运用MediaPipe Lite卷积网络达成30 fps人体33关键点的探测,并同步融合深度相机空间坐标以生成精确的3D关节数据;在映射层开创动态肩部参考校准机制,冲破传统标定的束缚,结合工作空间的双重限制来确保运动安全;在控制层构建几何闭式逆解模型(具备08 ms实时解算能力),借由双线程异步架构将姿态检测与关节控制分离开来,从根本上攻克响应延迟的瓶颈。经实验验证显示,该系统于JAKA Zu3平台达成末端轨迹跟踪误差减小、动作延迟比率极低、关节超程率降为零,适用于160~190 cm操作者的动态场景。日后可应用于诸如汽车装配线以及核废料处理等场景,为柔性制造供给高适应性的人机协同模式。 |
| 英文摘要: |
| A system of collaborative control for uncalibrated robotic arms, grounded in 3D vision guidance, is put forward herein. This addresses three central issues in the control system of industrial robotic arms: inadequacy in human-machine collaboration precision, latency in dynamic response, and deficiency of safety constraints. Lightweight deep-learning perception and real-time inverse kinematic control are innovatively integrated. The perception layer employs the MediaPipe Lite convolutional network to detect 33 key human-body points at 30 fps.It also concurrently fuses the spatial coordinates from the depth camera, generating accurate 3D joint data. The mapping layer initiates a dynamic shoulder-reference calibration mechanism. This overcomes the traditional reliance on calibration and combines dual constraints of the working space, ensuring motion safety. The control layer features a geometric closed inverse-solution model (real-time solution in 0.8 ms). Through a two-threaded asynchronous architecture, it separates attitude detection from joint control, thoroughly resolving the bottleneck of response delay. Experimental validation indicates that on the JAKA Zu3 platform, the system attains a reduced terminal-trajectory tracking error, an extremely low action-delay rate, and a zero joint-overtravel rate. It suits the dynamic scenario of operators with heights ranging from 160 to 190 cm.In the future, it can find applications in settings like automotive assembly lines and nuclear-waste treatment, offering a highly adaptable human-machine collaboration model for flexible manufacturing. |
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