Robot Task Location Optimization Using Geometric Approximation
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Solution Overview
Problem
Current robot programming systems lack the capability to effectively and automatically determine the optimum location and orientation of robots within their workspace for efficient task performance, leading to inefficient operations and long simulation times due to complex non-linear models and the need for extensive calculations.
Innovation Solution
A method that designs experiments with a reduced number of tests to determine the optimal position and orientation of a task relative to a robot, using a polynomial function to approximate cycle time, thereby reducing simulation time and eliminating the need for advanced optimization knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex non-linear models and extensive calculations are used to determine optimal robot location, then optimization precision is improved, but computation time increases significantly
Solution Approach 1:
The patent uses simple geometric shapes (spheres, cylinders, boxes) as surrogate models to represent the robot workspace and task locations. These simplified models can be quickly evaluated without complex calculations, providing approximate optimization results much faster than detailed non-linear models while sacrificing minimal precision.
Solution Approach 2:
The patent replaces complex mechanical simulation systems with analytical geometric calculations. Instead of using time-consuming dynamic simulations to evaluate robot performance, the method uses closed-form mathematical expressions based on geometric relationships between the robot base, workspace boundaries, and task locations, dramatically reducing computation time.
2Ease of operation
If automatic determination of optimal robot location is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent implements automatic optimization where the system determines optimal task locations and robot configurations without requiring user expertise in optimization theory. The automated algorithm independently evaluates geometric relationships and computes optimal positions, making the system self-sufficient and eliminating the need for manual intervention or specialized knowledge.
Solution Approach 2:
The patent transforms the complex optimization problem into a simplified parameter-based approach by defining key geometric parameters (robot base position, workspace boundaries, task coordinates) and using analytical relationships between these parameters to determine optimal configurations, avoiding the need for complex iterative optimization algorithms.
3Adaptability or versatility
If robot performs tasks at arbitrary locations, then adaptability is improved, but cycle time increases due to suboptimal positioning
Solution Approach 1:
The patent performs preliminary optimization of task locations before actual robot execution. By pre-calculating optimal positions based on geometric relationships and storing these optimized configurations, the system ensures that when tasks are executed, the robot operates from pre-optimized locations that minimize cycle time, rather than adapting to arbitrary positions during execution.
Solution Approach 2:
The patent implements a dynamic optimization approach where the system can adaptively determine optimal task locations based on current robot configurations and workspace conditions. The geometric optimization model allows real-time recalculation of optimal positions when robot parameters or task requirements change, maintaining both adaptability and efficiency.
Data Source
AI summary
A method for optimizing performance of a robot. At least one experiment is designed including at least two tests. Each test differs from at least one other test in the experiment regarding the location of the task in relation to the robot. The boundaries that are allowable for location of a task are calculated/determined. The effect on optimality for at least one test in the experiment is calculated/determined. The experimental data is fit to an algorithm. The optimal location of the task is calculated/determined.