Robot Control Parameter Optimization Under Performance Constraints
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Solution Overview
Problem
Existing technologies face challenges in appropriately setting control parameters for robots due to the complexity of robot operations compared to servo motors.
Innovation Solution
A method for setting a robot's control parameter involves receiving objective function and constraint condition settings, controlling the robot to execute work using candidate parameter values, measuring performance and constraint evaluation values, optimizing the parameter values, and displaying a correlation chart showing the values obtained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning is used to set control parameters for servo motors, then parameter setting can be automated, but the approach is insufficient for complex robot operations
Solution Approach 1:
The patent transforms the control parameter setting problem into a parameter optimization problem by defining objective functions and constraint conditions. The system automatically searches for optimal parameter values that satisfy multiple constraints, enabling adaptation from simple servo motors to complex robot operations through systematic parameter optimization rather than simple machine learning mapping.
Solution Approach 2:
The patent introduces an intermediary optimization processing system that acts as a bridge between the control parameters and the actual robot operations. This intermediary layer evaluates multiple candidate parameters against objective functions and constraint conditions, selecting optimal parameters that balance performance and constraints, thus enabling complex robot operations to be controlled systematically.
2Reliability
If multiple constraint conditions are imposed on control parameters, then operation safety is improved, but parameter optimization becomes more difficult
Solution Approach 1:
The patent segments the complex parameter optimization problem into manageable components by separating constraint conditions from the objective function. Each constraint condition (such as acceleration limits, torque limits, vibration thresholds) is evaluated independently, allowing the system to systematically handle multiple constraints without overwhelming complexity. This segmentation enables safe operation while maintaining optimization feasibility.
3Ease of manufacture
If conventional parameter setting methods are used, then implementation is simple, but appropriate parameter values cannot be determined for complex operations
Solution Approach 1:
The patent implements a self-service parameter optimization system that automatically determines appropriate control parameters without requiring manual expert intervention. The system defines objective functions and constraint conditions, then autonomously searches for optimal parameters that balance performance and safety. This self-service approach maintains implementation simplicity while achieving high parameter setting accuracy for complex robot operations.
Data Source
AI summary
A method of the present disclosure includes (a) receiving settings of an objective function and a constraint condition, (b) controlling a robot to execute work using a candidate value of a control parameter and measuring a performance index value for the objective function and a constraint evaluation value, (c) searching for a next candidate value of the control parameter by executing optimization processing using a value of the objective function, (d) obtaining the values of the objective function and the constraint evaluation values with respect to the plurality of candidate values by repeating (b) and (c), and (e) displaying a processing result containing a correlation chart showing the values of the objective function and the constraint evaluation values with respect to each of the plurality of candidate values.