Robot Placement Teaching Using Image-Based Area Estimation
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Solution Overview
Problem
Conventional methods for controlling robots in manufacturing settings, such as packing components, require specialist knowledge and are time-consuming due to the need for pre-setting correspondence relationships between trays and components, making it difficult to efficiently teach robots new placement tasks without expert intervention.
Innovation Solution
An information processing apparatus that acquires images of a target area and a supply area, estimates potential placement areas, and generates control values for a robot to convey and place objects, allowing users to set up robots without specialist knowledge by mimicking a model operation indicated by a user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robot control programs or remote operation are used to control robots for placing components, then the robot can perform repeated placement operations, but it takes time to complete setup and requires specialist knowledge
Solution Approach 1:
The system captures images of the actual tray and component arrangements and uses these images as teaching data for the robot. Instead of manually programming or remotely operating the robot, the system copies the visual information from the real workspace and processes it to generate robot control instructions automatically, eliminating the need for specialist knowledge and reducing setup time
Solution Approach 2:
The invention replaces manual programming operations and remote control operations with an automated image processing system. The system uses image capture, processing, and automatic generation of control instructions to substitute the mechanical/manual setup process, thereby reducing setup time and eliminating the need for specialist knowledge
2Adaptability or versatility
If correspondence relationships between trays and components are set in advance, then the robot can learn to hold components, but it requires time-consuming setup and expert intervention
Solution Approach 1:
The system enables the robot to automatically learn the correspondence relationships between trays and components by processing images of the actual arrangement. The robot performs self-learning through image analysis without requiring manual configuration or expert intervention, making the setup process simple and accessible to ordinary users
Solution Approach 2:
The system captures images of the actual tray and component arrangements and uses these images as teaching data. By copying the visual information from the real workspace and processing it automatically, the system eliminates the need for manual setup and expert knowledge, allowing anyone to teach the robot the correct placement patterns
Data Source
AI summary
An information processing apparatus includes an acquisition unit acquiring a first image and a second image, the first image being an image of a target area in an initial state, the second image being an image of the target area where a first object conveyed from a supply area is placed, an estimation unit estimating one or more second areas in the target area, based on a feature of a first area estimated using the first image and the second image, the first area being where the first object is placed, the one or more second areas each being an area where an object in the supply area can be placed and being different from the first area. A control unit controls a robot to convey a second object different from the first object from the supply area to any of the one or more second areas.