Robot Simulation Control for Axis Limits and Singularity Resolution
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Solution Overview
Problem
Conventional robotic simulation software lacks automation in simulating and analyzing parameter effects, fails to automatically correct issues like axis limit exceedance and singularity, and does not account for physical robot capabilities, requiring extensive user knowledge and a tedious trial-and-error process.
Innovation Solution
A computer-implemented method that performs multiple simulations with varying parameter values, automatically resolves issues like axis limit exceedance and singularity, and generates code for controlling robots based on user-selected simulations, incorporating inverse kinematics and accounting for robot capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional simulation software is used to simulate robotic operations, then simulation results can be obtained, but the process requires extensive user knowledge and is tedious and time-consuming due to manual parameter selection and iterative trial-and-error
Solution Approach 1:
The system performs preliminary actions by automatically generating multiple simulations with varying parameter values before user interaction. The simulation system proactively explores the parameter space and identifies promising configurations, eliminating the need for users to manually select parameters through trial-and-error. This preliminary automated exploration significantly reduces the time required for simulation setup and iteration.
Solution Approach 2:
The simulation system serves itself by automatically selecting parameter values, generating simulations, and analyzing results without requiring continuous user intervention. The system independently manages the iterative simulation process, automatically adjusting parameters based on previous results and identifying optimal configurations. This self-service capability eliminates the tedious manual parameter selection process and greatly improves simulation productivity.
2Extent of automation
If conventional simulation software is used, then simulations can be run, but the software does not automatically correct problems like axis limit exceedance or singularity, requiring manual analysis and parameter adjustment
Solution Approach 1:
The simulation system implements continuous feedback loops that automatically detect problems such as axis limit exceedance and singularity during simulations. When issues are detected, the system immediately adjusts parameters and re-runs simulations to correct the problems. This automated feedback mechanism eliminates the need for manual analysis and parameter adjustment, significantly reducing user effort while enhancing the extent of automation in the simulation process.
Solution Approach 2:
The system replaces the manual mechanical process of analyzing simulation results and adjusting parameters with an automated computational system. Instead of users manually examining simulation outputs and modifying parameters, the system uses algorithms to automatically detect issues, analyze causes, and adjust parameters programmatically. This substitution of manual operations with automated computational processes greatly improves ease of operation while increasing automation.
3Reliability
If conventional simulation software is used, then simulations can be performed, but the software does not account for physical robot capabilities like maximum speeds and accelerations, resulting in unrealistic simulations
Solution Approach 1:
The simulation system dynamically changes parameters based on physical robot capabilities. It incorporates maximum speeds, accelerations, and other operational constraints as bounding parameters that guide the simulation process. The system adjusts simulation parameters to ensure they remain within the physical capabilities of the actual robot, thereby improving the realism and reliability of simulations without requiring overly complex device integration.
Solution Approach 2:
The simulation system acts as an intermediary between the virtual simulation environment and the physical robot's capabilities. It translates physical constraints into simulation parameters and ensures that simulated operations are feasible on the actual robot. This intermediary layer reconciles the virtual and physical domains, improving simulation reliability by grounding virtual operations in physical reality while avoiding the need for direct complex integration with the physical robot system.
Data Source
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AI summary
Techniques are disclosed for controlling robots based on generated robot simulations. A robot simulation application is configured to receive a robot definition specifying the geometry of a robot, a list of points defining a toolpath that a head of the robot follows during an operation, and a number of simulations of the robot performing the operation. The simulation application then performs the number of simulations, displays results of those simulations, and generates code for controlling a physical robot based on a user selection of one of those simulations. During each simulation, if a robotic problem, such as an axis limit or a singularity problem, is encountered, then the simulation application attempts to resolve the problem by rotating the robot head in both directions about a tool axis and determining a smallest angle of rotation in either direction that resolves the robotic problem, if any.