简单易用的机器人3D视觉解决方案。我们提供标准解决方案，从而不需要额外的昂贵的视觉技术支持。用户不需要CAD模板，便可定义新工件，建立自己的系统。我们提供三款产品帮助您实现自动化码垛分拣任务。所有产品都包含完整的解决方案：3D相机、处理软件、工控机、校准工具及必要的线缆。

【实验教学目标 】

【主要仪器设备 】

该方案主要硬件包括：1台UR5机械臂（可以是UR其他型号），PICK-IT M-HD 3D视觉系统，一个2指或3指夹抓。通过UR+插件让两者完美结合，实现各类3D视觉分拣工作。 

【实验项目设置 】

THESIS CONTENTS

控制

1）移动机器人运动控制器设计

Röhrig C, Heß D, Künemund F. Motion controller design for a mecanum wheeled mobile manipulator[C]//2017 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2017: 444-449.

2）上肢跟踪的实时运动控制

Omarali B, Taunyazov T, Bukeyev A, et al. Real-Time Predictive Control of an UR5 Robotic Arm Through Human Upper Limb Motion Tracking[C]//Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. 2017: 237-238.

3）使用UR模仿人类写作

Miatliuk K, Wolniakowski A, Diaz M, et al. Universal robot employment to mimic human writing[C]//2019 20th International Carpathian Control Conference (ICCC). IEEE, 2019: 1-5.

运动学与动力学

1）基于运动学模型的UR5机械手标定

Liang B, Cheng Y, Zhu X, et al. Calibration of UR5 manipulator based on kinematic models[C]//2018 chinese control and decision conference (CCDC). IEEE, 2018: 3552-3557.

2）UR5动力学建模

Kebria P M, Al-Wais S, Abdi H, et al. Kinematic and dynamic modelling of UR5 manipulator[C]//2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, 2016: 004229-004234.

3）基于奇异性-鲁棒多任务优先级逆运动学框架

Moe S, Antonelli G, Pettersen K Y, et al. Experimental results for set-based control within the singularity-robust multiple task-priority inverse kinematics framework[C]//2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2015: 1233-1239.

4）逆运动学求解新方法

Moe S, Antonelli G, Teel A R, et al. Set-based tasks within the singularity-robust multiple task-priority inverse kinematics framework: General formulation, stability analysis, and experimental results[J]. Frontiers in Robotics and AI, 2016, 3: 16.

.视觉、标定、感知

1）基于UR的图像视觉自动校准系统

Jian B L, Tsai C S, Kuo Y C, et al. An image vision and automatic calibration system for universal robots[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2019: 1461348419874925.

2）基于UR的新规划方法

Tatsubori M, Munawar A, Moriyama T. Design and Implementation of Linked Planning Domain Definition Language[J]. arXiv preprint arXiv:1912.07834, 2019.

3）手眼标定

Li J, Li X, Dun A, et al. Hand-eye calibration for flexible manipulator[C]//Journal of Physics: Conference Series. IOP Publishing, 2019, 1187(3): 032097.

4）基于UR的自适应学习

Inoue T, Chaudhury S, De Magistris G, et al. Transfer learning from synthetic to real images using variational autoencoders for robotic applications[J]. arXiv preprint arXiv:1709.06762, 2017.

5）基于UR的视觉跟踪

Ramachandruni K, Jaiswal S, Shah S V. Vision-based control of UR5 robot to track a moving object under occlusion using Adaptive Kalman Filter[M]//Proceedings of the Advances in Robotics 2019. 2019: 1-6.

规划

1）路径规划算法的自动调整和配置

Burger R, Bharatheesha M, van Eert M, et al. Automated tuning and configuration of path planning algorithms[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 4371-4376.

2）机器人任务规划评估方法

Paxton C, Jonathan F, Hundt A, et al. Evaluating methods for end-user creation of robot task plans[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6086-6092.

3）预测控制的深度学习用于人体运动跟踪

Agravante D J, De Magistris G, Munawar A, et al. Deep learning with predictive control for human motion tracking[J]. arXiv preprint arXiv:1808.02200, 2018.

4）基于UR协作机器人的时间最优规划

Zhenyi C. Joint Trajectory Time Optimization of Cobot Based on Particle Swarm Optimization[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2019, 616(1): 012015.

5）多机械臂轨迹规划算法

Tavares P, Lima J, Costa P, et al. Multiple manipulators path planning using double A[J]. Industrial Robot: An International Journal, 2016.

医疗

1）基于UR的康复机器人

Kyrkjebø E, Laastad M J, Stavdahl Ø. Feasibility of the UR5 Industrial Robot for Robotic Rehabilitation of the Upper Limbs After Stroke[C]//2018 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2018: 1-6.