Robot Controller Grasp Pattern Selection for Sequential Operations
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Solution Overview
Problem
Industrial robots face challenges in determining efficient grasp techniques that do not obstruct subsequent operations, as existing methods either focus on presenting multiple grasp techniques or avoiding re-grasping, but not both efficiently.
Innovation Solution
A robot controller system that uses an input unit, database, and processing unit to select grasp patterns that enable sequential execution of operation instructions, considering both current and next states, to determine a grasp technique that avoids obstacles in subsequent operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot presents multiple grasp techniques to the user for selection, then the user has more options for grasping work, but the operation time increases and the final choice depends on user judgment rather than automated optimization
Solution Approach 1:
The robot controller automatically determines the optimal grasp technique by itself, eliminating the need for user selection. The system evaluates multiple grasp techniques and autonomously selects the most appropriate one based on the operation sequence, work properties, and robot configuration, thereby reducing operation time while maintaining adaptability.
Solution Approach 2:
The robot controller preliminarily evaluates multiple grasp techniques in advance by simulating the operation sequence and determining which grasp technique is most suitable before actual execution. This preliminary determination optimizes the grasp selection process and prevents time-consuming user deliberation.
2Productivity
If the robot avoids re-grasping when handing over work to a user, then the operation efficiency improves, but the solution is limited to handover scenarios only and does not address other operational situations
Solution Approach 1:
The robot controller applies a universal grasp determination method that works across multiple operational scenarios including handover operations, work transportation, and various manipulation tasks. The system evaluates grasp techniques based on the entire operation sequence rather than limiting to specific scenarios, making the solution adaptable to diverse robotic applications.
Solution Approach 2:
The robot controller uses feedback from the operation sequence and work properties to dynamically determine the optimal grasp technique. The system continuously evaluates whether the current grasp technique is appropriate for subsequent operations and adjusts the grasp strategy accordingly, enabling efficient operation across different scenarios.
3Ease of operation
If the robot selects a grasp technique based only on the current state, then the decision-making process is simple and fast, but the grasp technique may become an obstacle to subsequent operations
Solution Approach 1:
The robot controller preliminarily evaluates the suitability of grasp techniques by simulating the entire operation sequence in advance. The system determines whether a grasp technique will become an obstacle to subsequent operations before selecting it, ensuring reliability while maintaining computational efficiency through pre-planning.
Solution Approach 2:
The robot controller autonomously evaluates and selects the optimal grasp technique by considering the entire operation sequence, work properties, and robot configuration. The system performs self-assessment of grasp technique suitability without external intervention, ensuring both simplicity and reliability in the decision-making process.
Data Source
AI summary
A robot controller includes an input unit that receives operation instruction information, a database that stores grasp pattern information, and a processing unit that performs control processing based on information from the input unit and information from the database, and the input unit receives first to N-th operation instructions as operation instruction information, the processing unit loads i-th grasp pattern information that enables execution of the i-th operation instruction and j-th grasp pattern information that enables execution of the j-th operation instruction as the next operation instruction to the i-th operation instruction from the database, and performs control processing based on the i-th grasp pattern information and the j-th grasp pattern information.