Robot Arm Pose Teaching With Redundancy-Aware Axis Evaluation
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Solution Overview
Problem
Robot programming methods often face challenges in efficiently determining optimal axis positions for tasks with kinematic or task redundancy, leading to ambiguous or suboptimal robot poses, especially in applications like welding, drilling, and gluing where orientation tolerances are involved.
Innovation Solution
A method that involves manually or simulated evaluation of robot arm axis positions based on criteria such as distance from end stops, torque, energy efficiency, and manipulability, using a display device to guide the user in selecting favorable axis positions for programming, allowing for automatic control of the robot arm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robot programming uses manual teaching or automatic path generation without redundancy optimization, then the programming process is simpler, but the robot assumes suboptimal axis positions leading to compensating movements and reduced operational efficiency
Solution Approach 1:
The system automatically optimizes axis positions for redundant robot structures without requiring manual intervention. The control device independently calculates optimal positions within the redundancy space, allowing the robot to serve itself by eliminating the need for complex manual programming of compensating movements
Solution Approach 2:
The system changes the optimization parameters dynamically based on the current robot state and task requirements. By adjusting the quality function parameters that define optimality (such as avoiding singularities, minimizing torque, maintaining distance from obstacles), the system adapts to different operational contexts while maintaining optimal performance
2Measurement precision
If the system provides detailed evaluation and optimization of axis positions for redundant robots, then operational efficiency improves, but the control and programming complexity increases
Solution Approach 1:
The system implements feedback by continuously evaluating the quality of axis positions during operation. The control device monitors the current state, compares it against optimization criteria defined in the quality function, and automatically adjusts positions to improve metrics such as distance from singularities and torque distribution
Solution Approach 2:
The patent introduces an intermediary optimization layer between the high-level task specification and low-level motor control. This intermediate control layer handles the complexity of redundancy resolution by translating task requirements into optimized axis positions, shielding the user from complex control details while maintaining precision
3Loss of time
If robot arm assumes optimal axis positions through redundancy optimization, then compensating movements are reduced, but calculating and determining optimal positions requires additional computational resources and time
Solution Approach 1:
The system performs preliminary optimization calculations during the programming phase and at the beginning of each operation. By pre-calculating optimal axis positions and storing them in the quality function, the system avoids real-time computational overhead during execution, reducing the need for compensating movements without requiring continuous high computational power
Solution Approach 2:
The optimization focuses on local quality improvements at critical points such as avoiding singularities and maintaining safe distances from obstacles. Rather than optimizing all parameters simultaneously with high computational effort, the system prioritizes local optimizations that have the greatest impact on operational efficiency, reducing overall computational requirements
Data Source
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Figure 3
AI summary
The method involves manually moving a robot arm (M), so that a fastening device (9) i.e. flange, and/or a tool center point (TCP) associated with the arm takes a preset pose. Positions of axles (A) of the arm for the pose are moved with respect to links (1-4) by an electric drive. Actual position of the axles of the arm is evaluated based on a preset criterion by a control device (S) and a computing device (17). Result of evaluation of the position of the axles is displayed by a display device (16) i.e. touch screen. The position of the axles is manually changed.