Robotic Workcell Layout Using Grid-Based Robot Performance Evaluation
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Solution Overview
Problem
Conventional optimization processes for determining the ideal base location for a robot within a robotic workcell are computationally complex, time-consuming, and limited in handling multiple optimization goals, making them unsuitable for design iteration and practical application.
Innovation Solution
A computer-implemented method that determines a plurality of locations within a workcell volume, evaluates robot-motion attributes, and computes performance metrics for each location, allowing users to objectively compare robot performance and rapidly generate solutions across the entire workcell.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optimization processes are used to determine ideal robot base location, then measurement precision of robot performance is improved, but device complexity and computation time increase significantly
Solution Approach 1:
The patent segments the workcell volume into a discrete grid of candidate locations. Each grid point is evaluated independently using pre-computed robot trajectories and performance metrics. This segmentation transforms the continuous optimization problem into discrete, parallelizable evaluations, reducing computational complexity while maintaining measurement precision through sufficient grid resolution.
Solution Approach 2:
The patent performs preliminary computation of robot trajectories and performance metrics for each candidate location before final optimization selection. By pre-computing these values and storing them in data structures, the system avoids redundant calculations during the optimization process, significantly reducing overall computation time while preserving accurate performance measurements.
2Manufacturing precision
If conventional optimization processes are used, then robot placement accuracy is improved, but productivity and iteration speed deteriorate
Solution Approach 1:
By dividing the workcell into discrete grid locations, the patent enables parallel evaluation of multiple candidate positions simultaneously. This segmentation allows rapid computation of performance metrics for each location without requiring sequential optimization iterations, thereby maintaining placement accuracy while dramatically improving design iteration speed and productivity.
Solution Approach 2:
The patent creates simplified representations (copies) of robot performance characteristics for each candidate location using pre-computed trajectories and metrics. These copied performance data structures allow rapid comparison and selection of optimal locations without repeated complex simulations, accelerating the design iteration process while preserving placement accuracy.
3Measurement precision
If conventional optimization processes are used, then robot performance measurement is improved, but adaptability to multiple optimization goals deteriorates
Solution Approach 1:
The patent implements a universal evaluation framework that computes multiple performance metrics (e.g., reachability, motion smoothness, energy efficiency) for each candidate location using the same grid-based approach. This multi-functional system can handle various optimization goals simultaneously by weighting and combining different metrics, providing both precise measurement and high adaptability to different optimization objectives.
Data Source
AI summary
A computer-implemented method for generating and evaluating robotic workcell solutions includes: determining a plurality of locations within a workcell volume, wherein each location corresponds to a possible workcell solution; for each location included in the plurality of locations, determining a value for a first robot-motion attribute for a first robot based on position information associated with the location and a trajectory associated with a component of the first robot; and, for each location included in the plurality of locations, computing a first value for a first performance metric based on the value for the first robot-motion attribute.