Collision Sphere Generation for Efficient Robot Mesh Collision Checks
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Solution Overview
Problem
Conventional collision check systems for autonomous machines require significant computing resources due to the use of mesh-based representations, which can be inefficient and resource-intensive, particularly when optimizing collision spheres for machine navigation.
Innovation Solution
A method for generating collision spheres using a maximum number of points and an overshoot distance, combined with point-based sampling and pruning, to optimize the set of spheres for machine representation, reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mesh-based representation is used for collision checks, then collision detection accuracy is improved, but computing resources and processing time increase significantly
Solution Approach 1:
The machine geometry is segmented into multiple convex components, with each component represented by a separate collision sphere. This segmentation allows the complex mesh geometry to be approximated by simpler spherical shapes, reducing computational complexity while maintaining adequate collision detection accuracy.
Solution Approach 2:
Instead of using the detailed mesh representation directly for collision checks, a simplified copy in the form of collision spheres is created. These spheres replicate the essential collision characteristics of the machine components without requiring the full mesh detail, thereby reducing computing resources while preserving collision detection functionality.
2Use of energy by moving object
If collision spheres are used to represent machine components, then computing resources are reduced, but representation accuracy decreases due to overshoot distance
Solution Approach 1:
The system dynamically adjusts the number and positioning of collision spheres based on the specific geometry and operational requirements. Rather than using a fixed number of spheres, the algorithm determines the optimal set of spheres adaptively, allowing the representation accuracy to be optimized for each specific machine configuration while maintaining computational efficiency.
Solution Approach 2:
The invention changes key parameters such as sphere radius and center positions to minimize overshoot distance. By optimizing these parameters, the collision spheres more accurately fit the actual machine geometry, reducing the gap between the simplified spherical representation and the detailed mesh while maintaining the computational advantages of sphere-based collision detection.
3Quantity of substance
If conventional sphere optimization methods are used, then sphere set is reduced, but unnecessary operations increase computing resource requirements
Solution Approach 1:
The invention extracts only the essential operations needed for sphere optimization, removing unnecessary steps from conventional methods. Specifically, it eliminates redundant sphere removal operations and focuses on generating a minimal set of spheres that adequately represent the machine geometry, thereby reducing both the number of spheres and the computing resources required for optimization.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing the collision sphere set during machine setup or initialization. This preliminary generation of the sphere set avoids the need for repeated optimization computations during runtime collision detection, significantly reducing computing resource requirements during actual operation while maintaining adequate representation accuracy.
Data Source
AI summary
In various examples, determining collision spheres for machines and applications is described herein. Systems and methods described herein use one or more parameters, such as a maximum number of points and/or an overshoot distance, to determine a candidate set of spheres associated with a mesh of an object. For instance, the maximum number of points may be used to generate a grid of points associated with the mesh and the overshoot distance may be used to then generate the candidate set of spheres that are centered at the points included in the grid. The systems and methods described herein may then sample a number of points located on a surface of the mesh and use the sampled points to remove (e.g., prune) one or more spheres from the candidate set of spheres in order to generate a final set of spheres for the object.