Robotic Workcell Layout Using Grid-Based Robot Placement 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 in handling multiple optimization goals, making them unsuitable for design iteration and scalable solutions.
Innovation Solution
A computer-implemented method that determines multiple locations within a workcell volume, calculates robot-motion attributes, and computes performance metrics based on user-defined criteria, enabling objective comparison and rapid generation of solutions for robot or workpiece locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optimization processes are used to determine robot base location, then solution accuracy is improved, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the workcell volume into a discrete grid of candidate locations. Instead of treating robot placement as a continuous optimization problem, the space is divided into discrete cells that can be evaluated independently. This segmentation transforms the complex continuous optimization into multiple simpler discrete evaluations, reducing computational complexity while maintaining sufficient accuracy for practical applications.
Solution Approach 2:
The patent creates virtual copies of the robot at each grid location to evaluate performance. Rather than solving one complex optimization problem, multiple simplified simulations are performed with the robot copied to different positions. Each copy is evaluated against the same task requirements, allowing parallel assessment of multiple locations without the computational burden of iterative optimization at each point.
2Measurement precision
If conventional optimization processes are used to determine robot base location, then solution accuracy is improved, but time consumption increases making it unsuitable for design iteration
Solution Approach 1:
The patent performs preliminary discretization of the workcell space into candidate locations before the actual evaluation. By pre-defining the grid of possible robot positions and evaluating all of them in advance, the system eliminates the need for time-consuming iterative optimization during the design iteration phase. Users can quickly compare pre-evaluated locations and make informed decisions without waiting for complex optimization computations.
3Reliability
If conventional optimization processes are designed for specific optimization goals, then optimization performance for that goal is improved, but adaptability to different goals deteriorates
Solution Approach 1:
The patent creates a universal evaluation framework that can assess robot performance for multiple different optimization goals using the same grid-based approach. The system calculates various performance metrics (such as task completion time, energy consumption, motion smoothness) for each candidate location, allowing users to evaluate locations based on different goals without redesigning the optimization process. This multi-functional evaluation system adapts to different objectives by simply changing which metric is prioritized.
Data Source
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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.