Obstacle Collision Detection Using Topological Skeleton Points
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Solution Overview
Problem
Existing methods for determining obstacle collision in object moving paths require extensive calculations and operations, making them inefficient, especially when all points in the path area are considered.
Innovation Solution
The method uses topological skeleton points to select target points based on error values, modeling the path area with filling circles to determine obstacle collision, thereby reducing the number of operations and adjusting modeling error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all points in the path area are used to determine obstacle collision, then measurement precision is improved, but device complexity and computational burden increase
Solution Approach 1:
The path area is segmented into a topological skeleton structure that captures essential geometric features. Instead of processing all points in the continuous path area, the method extracts a simplified skeletal representation that preserves collision detection capability while reducing the number of points to be processed, thereby lowering computational complexity.
Solution Approach 2:
Key points are extracted from the topological skeleton based on error values that indicate their importance for accurate collision detection. By selecting only the most critical points rather than all skeleton points, the method maintains measurement precision for collision detection while significantly reducing the computational burden of processing excessive points.
2Productivity
If topological skeleton points are used instead of all path area points, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
Different points on the topological skeleton are assigned different qualities based on their error values. Points with higher error values (indicating greater importance for accurate path representation) are selected for collision detection, while points with lower error values are discarded. This local quality differentiation ensures that precision is maintained at critical locations while improving overall productivity.
Solution Approach 2:
The method changes the parameter of point selection by using error values as a criterion. Instead of uniformly processing all skeleton points or using a fixed number of points, the error value parameter dynamically determines which points are selected, allowing the system to adaptively balance between productivity and measurement precision based on the specific path geometry.
Data Source
AI summary
A method of determining obstacle collision by using an object moving path includes: acquiring a topological skeleton corresponding to a path area on an image including a moving path of an object and an obstacle; determining, from among skeleton points forming the topological skeleton, branch points that are interconnecting points between branches of the topological skeleton; determining a target branch from among the branches by using the branch points; selecting, from among points forming the target branch, a plurality of target points to determine whether the moving path is a collision path of the object; and determining whether the moving path is the collision path by using the target points.