Examples of robot grasping and positioning vision application solutions

Program overview

The robot grasping and positioning vision application solution example is an example of a public industry application solution in the direction of robot visual perception. For loading, sorting, assembly and handling stations, a system solution for robot visual positioning, hand-eye calibration, attitude estimation and grab point output is presented. This page is geared towards project evaluation and solution matching. It does not disclose customer names, production line scales, business data or unconfirmed operating indicators. It focuses on what similar computer vision projects need to be evaluated, how to organize the technical route, how to confirm the delivery content, and what information should be prepared for early communication.

Project background

Robot vision projects need to convert image perception results into executable spatial coordinates and attitude information. Different from simple detection, robot scenarios also need to focus on calibration, coordinate conversion, grasping point selection, path obstacle avoidance, and interface stability with the robot control system. In project communication, it is first necessary to clarify the detection objects, work station locations, sample differences, rhythm requirements, installation space, existing equipment interfaces and acceptance methods. For scenarios such as disordered grabbing, loading and unloading positioning, assembly guidance, and mobile robot perception, the same visual concept may require different camera angles, light source methods, lens focal lengths, trigger methods, and judgment rules at different sites. Therefore, JIVISION usually starts with sample analysis and imaging verification, rather than directly applying a fixed template.

Customer needs and assessment points

Typical requirements for this type of project include target positioning, pose estimation, hand-eye calibration, and grasping guidance. In addition to the identification itself, it is also necessary to evaluate whether the test results need to retain pictures, whether the formula needs to be switched according to product model, whether it needs to be connected with PLC, robot, MES, WMS or host computer system, and whether batch reports need to be generated. The main pain points include: the material posture is not fixed, and the adaptability of traditional fixtures or fixed point methods is insufficient; scattered stacking, reflective surfaces and occlusions will affect the determination of grab points; the vision system needs to stably transmit coordinates and status to the robot controller. These issues need to be confirmed through sample testing and field condition analysis, and are not suitable to be replaced by unverified universal indicators.

Technical solution

The technical solution revolves around hand-eye calibration, pose estimation, and grasping and positioning, and is implemented in conjunction with the project delivery process. JIVISION will first evaluate the imaging stability, and then design the algorithm link and system interface. The plan usually includes: selecting the 2D, In actual projects, traditional image processing, deep learning detection, OCR recognition, instance segmentation, point cloud processing or rule review processes can be combined according to object characteristics, so that the system can both identify targets and explain the basis for judgment.

System architecture

• RGB-D or 3D collection
• Pose estimation algorithm
• Coordinate conversion module
• robot interface

Implementation process

The implementation process generally includes requirement confirmation, sample collection, imaging experiments, solution verification, algorithm training or rule development, interface definition, on-site deployment, acceptance testing, and operation and maintenance handover. At each stage, sample conditions, parameter versions, judgment rules, and abnormal sample records will be precipitated to facilitate subsequent review. When customers subsequently add product models, defect categories or on-site workstations, they can continue to iterate based on the original data and interfaces.

Delivery content

• Positioning recognition algorithm
• Hand-eye calibration process
• Robot interface adaptation
• Work station debugging documentation

Acceptance and iteration

The acceptance indicators should be determined based on customer samples, on-site test results and testing standards confirmed by both parties. Common evaluation dimensions include recognition accuracy, risk of missed detection, false alarm handling, processing speed, stability under illumination changes, data traceability and maintenance convenience. The solution example description on the public page is used to illustrate the technical route and delivery method. The final project indicators should be based on the customer's real samples, deployment environment and contract agreements.

Applicable scenarios

This solution example is suitable for disordered grabbing, loading and unloading positioning, assembly guidance, mobile robot perception and similar needs. It is also suitable for reference by customers who are evaluating machine vision inspection, computer vision algorithms, edge AI vision, 3D visual measurement, robot vision or visual data training. For projects with uncertain technical routes, sample evaluation, imaging testing, and small-scale solution verification can be conducted first to confirm feasibility before proceeding to system development and on-site deployment.

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