Offline Robot Teaching with Interpolation-Based Collision Checking
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Solution Overview
Problem
Existing simulation devices for robots fail to efficiently detect and prevent interference between the robot and peripheral devices during motion planning, requiring extensive and time-consuming 3D model-based checks.
Innovation Solution
An offline teaching device generates interpolation points on a robot's motion route, detects interference between these points and peripheral devices, and corrects the motion program to avoid collisions by adding intermediate points, reducing the need for detailed 3D model checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 3D models of robot and peripheral device are disposed in virtual space for interference detection, then interference detection capability is improved, but detection time and computational load increase significantly
Solution Approach 1:
The patent segments the interference detection process into two distinct phases: (1) rough detection using simplified line segments representing robot linkages, and (2) detailed detection using accurate 3D models only at potentially interfering positions. This segmentation allows most of the motion route to be checked quickly with simple geometry while maintaining comprehensive interference detection coverage.
Solution Approach 2:
The patent applies partial action by performing detailed 3D model interference detection only at specific locations where interference is suspected (based on rough detection results), rather than performing detailed detection along the entire motion route. This reduces computational load while maintaining detection reliability.
2Measurement precision
If detailed 3D model checks are performed along entire motion route, then detection precision is improved, but productivity decreases due to time consumption
Solution Approach 1:
The detection route is segmented into multiple sections, with different detection methods applied to different segments. Simplified line segment-based detection is applied to most segments, while detailed 3D model detection is applied only to specific segments where interference risk is identified.
Solution Approach 2:
Detailed 3D model detection is performed partially only at necessary locations (potentially interfering positions) rather than excessively along the entire motion route, achieving adequate detection precision with reduced computational effort.
3Reliability
If comprehensive interference detection is performed, then motion safety is improved, but device complexity increases due to multiple detection methods
Solution Approach 1:
The detection system is segmented into two hierarchical levels: a first-level rough detection using simple line segment models, and a second-level detailed detection using accurate 3D models. This segmentation allows the system to maintain motion safety through comprehensive detection while managing complexity by using simple methods for most of the detection process.
Solution Approach 2:
The line segment-based rough detection acts as an intermediary that filters the motion route to identify potentially interfering positions, enabling the detailed 3D model detection to focus only on critical areas rather than the entire route, thus reducing overall system complexity.
Data Source
AI summary
An offline teaching device for reducing an amount of time required to generate a motion route with which interference could be avoided, the offline teaching device including at least one processor. The processor generates, as a result of a motion program that includes a plurality of teaching points being input, numerous interpolation points on a motion route of a tool distal-end point of a robot, the motion route being formed among the teaching points in accordance with the motion program; and detects whether interference occurs between each of the generated interpolation points and a peripheral device.