Robot Learning Control Unit for Vibration Reduction
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Solution Overview
Problem
Existing robot systems face increased learning control iterations when designed for wide use ranges with varying orientations and end effector loads, leading to inefficiencies in vibration reduction.
Innovation Solution
A robot system with a learning control unit that includes multiple learning control parts assigned to specific use ranges, allowing for selection based on operation information such as position and load, to optimize vibration correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single learning control part is designed to ensure robustness across a wide use range with varying orientations and end effector loads, then the vibration reduction effectiveness is maintained, but the number of learning control iterations required increases
Solution Approach 1:
The learning control unit is divided into multiple learning control parts, each responsible for a specific use range defined by orientation and end effector load conditions. This segmentation allows each part to specialize in correcting vibrations for particular operational scenarios, reducing the iterations needed compared to a single universal learning control part that must handle all conditions.
Solution Approach 2:
Different learning control parts are assigned to different use ranges (local conditions), with each part optimized for its specific orientation and load range. This local optimization ensures that each learning control part can achieve effective vibration reduction with fewer iterations for its designated range, rather than requiring many iterations across all possible conditions.
2Productivity
If the robot operation is accelerated to shorten tact time, then the production efficiency is improved, but vibrations are generated at the hand tip portion due to reducer strain and arm rigidity shortage
Solution Approach 1:
Acceleration sensors are attached to the hand tip portion to detect vibrations during robot operation. The detected vibration data is fed back to the learning control unit, which calculates correction amounts and applies them to the servo control. This feedback mechanism enables the system to identify and correct vibrations generated during high-speed operation, allowing accelerated robot movement without excessive hand tip vibrations.
Solution Approach 2:
The learning control performs preliminary vibration correction by calculating correction amounts based on detected vibrations and applying them before actual high-speed operation. This preliminary action allows the robot to be pre-adjusted for optimal performance at accelerated speeds, enabling production efficiency improvement while preventing harmful vibrations during actual operation.
3Productivity
If learning control is not terminated, then the robot cannot start actual operation, but sufficient vibration correction may not be achieved
Solution Approach 1:
The learning control is configured to terminate after a predetermined number of iterations or when vibration correction reaches a satisfactory level. This partial action approach balances the need to enable actual operation (productivity) with achieving sufficient vibration correction (reliability). The system performs enough learning control iterations to achieve acceptable vibration reduction without indefinitely delaying operation startup.
Data Source
AI summary
A robot system is provide with a robot control device that includes an operation control unit and a learning control unit. The learning control unit performs a learning control in which a vibration correction amount for correcting a vibration generated at a control target portion of a robot is calculated and the vibration correction amount is employed in the operation command at a next time. The learning control unit includes a plurality of learning control parts for calculating the vibration correction amount and a selection unit that selects one of the plurality of learning control parts on the basis of operation information of the robot when the robot is made to be operated by an operation program that is a target of the learning control.