Mechanical arm assembly based on OpenMV visual perception and the 51 SCM
With OpenMV as the core component and 51 single-chip microcomputer as the control core, we carried out a deep research on autonomous grasping technology of robot arm. In this research project, we mainly use OpenMV camera to visually identify the weight of the object. In this way, the robot arm can accurately identify the weight of the object. At the same time, we use 51 single-chip microcomputer as the control core, responsible for the robot arm for precise operation control, so as to achieve accurate grasp of the object and precise positioning.In the course of research, we firstly expatiate on the working mechanism of robot arm, including the motion principle of each joint and how to realize accurate grasping through control algorithm. Then, we design the OpenMV visual control module to ensure that it can efficiently process image data and accurately identify the object weight information. In addition, we design the hardware circuit of the 51 MCU and write the corresponding control program to ensure that it can accurately control the joints of the robot arm according to the data provided by OpenMV.Through the combination of OpenMV vision module and 51 single-chip microcomputer control core, we successfully implemented a robot arm system which can grasp and locate objects autonomously. The system not only improves the accuracy and efficiency of the grasping task, but also has a good expansibility and application prospects.
Authors
- Bin Gao (ORCID: https://orcid.org/0000-0003-3377-6895)
- Zhiying Wang (ORCID: https://orcid.org/0000-0003-3339-3085)
- Yu Mei (ORCID: https://orcid.org/0009-0008-3126-2974)
- Qi Wang (ORCID: https://orcid.org/0000-0001-6940-6533)
- Junlong Wang (ORCID: https://orcid.org/0000-0002-1639-2438)
- Minge Chen (ORCID: https://orcid.org/0000-0003-1784-3832)
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/ricai64321.2024.10911384
- Citations
- 1
- Primary Topic
- Manufacturing Process and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.35