Robotic Workcell Layout Using Discrete Multi-Metric Location Evaluation
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Solution Overview
Problem
Conventional optimization processes for determining the ideal base location of a manufacturing robot in a robotic workcell are computationally complex, time-consuming, and limited to single optimization goals, making them unsuitable for design iteration and multi-objective optimization scenarios.
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 across different locations based on user-defined metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optimization processes are used to determine ideal robot base location, then optimization accuracy can be achieved, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the workcell volume into a discrete grid of locations, transforming the continuous optimization problem into a discrete evaluation problem. Each grid location is independently evaluated using pre-computed reachability masks and performance metrics, avoiding the need for complex continuous optimization algorithms.
Solution Approach 2:
The patent performs preliminary computations by pre-computing reachability masks for each robot configuration and storing performance metric calculations for all possible locations. This allows the final location selection to be made rapidly by simply querying pre-computed data rather than performing complex optimization calculations in real-time.
2Measurement precision
If conventional optimization processes are used to determine ideal robot base location, then optimization accuracy can be achieved, but time consumption increases making it unsuitable for design iteration
Solution Approach 1:
The workcell is segmented into discrete grid locations, allowing parallel evaluation of multiple candidate positions. This discretization enables the system to rapidly assess many locations without the iterative computational burden of continuous optimization methods.
Solution Approach 2:
Performance metrics and reachability information are pre-computed and stored for all possible locations before the actual design selection process. This eliminates the need for time-consuming optimization calculations during design iteration, enabling rapid comparison of different robot base locations.
3Measurement precision
If conventional optimization processes are designed for specific optimization goals, then optimization performance for those goals is improved, but adaptability to different optimization goals decreases
Solution Approach 1:
The patent creates a universal evaluation framework that computes multiple performance metrics (reachability, motion smoothness, energy efficiency, etc.) simultaneously for all locations. Users can select and weight different metrics based on their specific optimization goals, making the same system adaptable to various objectives without requiring separate optimization processes.
Solution Approach 2:
The system allows dynamic configuration of performance metrics and their weights based on user-defined optimization goals. The evaluation criteria can be adjusted in real-time to match different task requirements, enabling the same computational framework to optimize for different objectives by simply changing the metric weights rather than redesigning the optimization process.
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.