Robot Handling System with Adaptive End Effector Selection
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Solution Overview
Problem
Current handling systems face inefficiencies in handling new items, requiring significant time and effort due to the need for system reconfiguration and lack of adaptability.
Innovation Solution
A robot integrated management system that includes a master device, slave devices, and an integrated management database, which coordinates robots and material handling machinery to optimize operations, such as delivery, picking, and packing, by using end effectors with suction, jamming, or pinching mechanisms, and employing cameras for item recognition and data analysis to determine the best grasping strategy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the handling system uses a fixed configuration with specific end effectors for specific item types, then the reliability of handling known items is improved, but the adaptability to new item types deteriorates
Solution Approach 1:
The system employs multiple types of end effectors (suction type, jamming type, pinching type) that can be selectively applied to different item types. The master device manages this diversity of end effectors, enabling the system to handle various items including soft items, rigid items, and items of different shapes and sizes, thus achieving both reliability for known items and adaptability to new items.
2Manufacturing precision
If the system requires manual reconfiguration when handling new item types, then the precision of handling is improved, but the time and effort required deteriorates
Solution Approach 1:
The system automatically determines the item type using image recognition technology and autonomously selects the appropriate end effector and handling parameters. This self-service capability eliminates the need for manual reconfiguration, allowing the system to quickly adapt to new item types while maintaining high handling precision through automated parameter optimization.
Solution Approach 2:
The master device pre-stores multiple types of end effectors and handling parameters in its database. When a new item type is introduced, the system performs preliminary automatic selection and configuration based on image recognition, preparing the optimal handling setup before actual operation begins, thereby reducing reconfiguration time while ensuring precision.
3Adaptability or versatility
If the system uses multiple types of end effectors for different item types, then the adaptability to various items is improved, but the device complexity increases
Solution Approach 1:
The system segments the handling function into distinct end effector types (suction, jamming, pinching), each optimized for specific item categories. The master device manages this segmentation by automatically selecting the appropriate end effector type based on item characteristics, thereby achieving comprehensive item coverage while keeping the control architecture manageable through centralized intelligence.
4Productivity
If the system automatically selects end effectors based on item recognition, then the productivity is improved, but the measurement precision requirement increases
Solution Approach 1:
The system replaces manual item identification and end effector selection with image recognition technology and automated control algorithms. This substitution enables rapid automatic selection of appropriate end effectors based on visual analysis of item characteristics, significantly improving productivity while the high precision of modern image recognition systems meets the demanding measurement accuracy requirements for reliable automatic decision-making.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system minimizes reconfiguration efforts and enhances handling efficiency by automatically selecting the appropriate end effector and operation mode based on item type, reducing handling time and improving processing accuracy.
Implementation Method 1
an end effector to be used for each object 10; wherein the end effector is one of a suction type end effector having a suction mechanism, a jamming type end effector having a jamming mechanism, and a pinching type end effector having a pinching mechanism
Implementation Method 2
an end effector to be used for each object 10; wherein the end effector is one of a suction type end effector having a suction mechanism, a jamming type end effector having a jamming mechanism, and a pinching type end effector having a pinching mechanism
Implementation Method 3
an end effector to be used for each object 10; wherein the end effector is one of a suction type end effector having a suction mechanism, a jamming type end effector having a jamming mechanism, and a pinching type end effector having a pinching mechanism
Data Source
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AI summary
In an arrangement, a robot system includes a robot that has a movable part and an end effector, and that grasps and moves an object; includes a sensor that measures the object; and is capable of processing the object in a plurality of operation modes. A data analyzing unit analyzes first-type data of the object obtained by the sensor by measuring the object, and calculates second-type data of the object. A database is used to store the second-type data of the object in a corresponding manner to the state of the data of the object, which is decided according to the operation mode of the robot system at the time of obtaining the first-type data of the object. With respect to the robot system which processes the object, an operation instructing unit gives an operation instruction to process the object according to an action command decided according to the second-type data of the object in the operation mode decided according to the state of the object stored in the database.