Robot Point-Set Interference Checking for Real-Time Motion Planning
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Solution Overview
Problem
Existing robot interference checking techniques in industrial settings are either computationally intensive, inaccurate due to approximations, or require significant time and effort to set up, making real-time collision avoidance challenging, especially in complex multi-robot and multi-obstacle environments.
Innovation Solution
A robot interference checking method using point sets represented as 1D indices, where CAD models of robots and obstacles are converted to 3D points, updated at each time step, and then converted to 1D indices for efficient storage and computation, allowing for fast interference checking by intersection of 1D index sets and swept volume computation by union across time steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If geometry primitives are used to approximate robot arms and obstacles, then the interference checking calculation complexity is reduced and computation speed is improved, but accuracy deteriorates due to approximation errors and false positive interference conditions
Solution Approach 1:
The patent segments the continuous CAD model geometry into discrete point sets sampled from the surfaces and volumes of robot arms and obstacles. This segmentation allows the system to represent complex geometries as collections of discrete points that can be efficiently processed through set operations, achieving both computational speed and accuracy without requiring geometric approximation.
Solution Approach 2:
The patent creates a computational copy of the CAD models by sampling points from the original geometry. These point sets serve as simplified representations that retain the essential geometric features needed for interference detection while being much faster to process than the full CAD models, effectively copying only the necessary information for collision detection.
2Measurement precision
If CAD models are used for interference checking, then accuracy is improved by avoiding geometry primitive approximations, but computational time increases making real-time motion planning impractical
Solution Approach 1:
The patent segments the continuous CAD model geometry into discrete point sets sampled from the surfaces and volumes of robot arms and obstacles. This segmentation allows the system to represent complex geometries as collections of discrete points that can be efficiently processed through set operations, achieving both computational speed and accuracy without requiring geometric approximation.
Solution Approach 2:
The patent changes the representation parameter from continuous CAD model geometry to discrete point sets with controlled density. By adjusting the sampling density parameter, the system can balance between accuracy (higher density) and computational speed (lower density), enabling real-time performance while maintaining sufficient detection accuracy.
3Productivity
If geometry primitives are defined around each obstacle and robot arm, then the setup process becomes tedious and time-consuming, but the interference checking computation becomes manageable
Solution Approach 1:
The patent implements self-service by automatically generating point sets directly from the CAD models without requiring manual definition of geometry primitives. The system automatically samples points from the robot arm and obstacle CAD models, eliminating the tedious manual setup process while maintaining computational efficiency through the inherent structure of the point set representation.
Solution Approach 2:
The patent creates a universal point set representation that works for all types of robot arms and obstacles without requiring type-specific setup procedures. This universal approach eliminates the need for different setup processes for different object types, making the system easy to use while maintaining computational efficiency across diverse applications.
Data Source
AI summary
A robot interference checking motion planning technique using point sets. The technique uses CAD models of robot arms and obstacles and converts the CAD models to 3D point sets. The 3D point set coordinates are updated at each time step based on robot and obstacle motion. The 3D points are then converted to 3D grid space indices indicating space occupied by any point on any part. The 3D grid space indices are converted to 1D indices and the 1D indices are stored as sets per object and per time step. Interference checking is performed by computing an intersection of the 1D index sets for a given time step. Swept volumes are created by computing a union of the 1D index sets across multiple time steps. The 1D indices are converted back to 3D coordinates to define the 3D shapes of the swept volumes and the 3D locations of any interferences.