Adaptive Collision Detection Area for Virtual Objects
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Solution Overview
Problem
Existing collision determination technologies in virtual spaces, particularly in two-dimensional and three-dimensional environments, face challenges in accurately determining collisions while minimizing computational overhead, often requiring excessive calculations or failing to account for complex object interactions.
Innovation Solution
A collision determination program that sets adaptive collision determination areas based on the state of objects, allowing for shape and size adjustments relative to their position and interaction status, using spherical, rectangular, or elliptical areas to determine overlaps and reduce computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If objects are approximated to simple spherical shapes for collision determination, then the amount of calculations is reduced, but collision determination accuracy deteriorates
Solution Approach 1:
The patent applies dynamics by making the collision determination area adaptable rather than fixed. The area's shape, size, and position are dynamically adjusted based on the object's state (e.g., movement speed, direction, and interaction context). This allows the system to use simple geometric shapes for computational efficiency while maintaining high collision detection accuracy through state-based adaptation.
Solution Approach 2:
The patent changes parameters of the collision determination area (shape, size, position) according to the object's state. By modifying these parameters dynamically, the system can accurately represent complex objects in simplified forms for collision detection, resolving the contradiction between calculation simplicity and detection accuracy.
2Measurement precision
If multiple spherical shapes are used to approximate one object for better collision determination, then collision determination accuracy is improved, but the amount of calculations increases
Solution Approach 1:
The patent segments the collision determination into two parts: a simplified collision determination area (single shape) for quick overlap checking, and detailed object state analysis for accuracy adjustment. This segmentation allows the system to perform fast initial collision checks using simple geometry while maintaining accuracy through state-based refinement, avoiding the need to check multiple spherical shapes simultaneously.
Solution Approach 2:
The patent creates a simplified copy (collision determination area) of the object that represents its collision characteristics without requiring complex geometry. This copy is dynamically adjusted based on object state, providing accurate collision detection while maintaining simple computational complexity.
3Device complexity
If fixed collision determination areas are used for all objects, then device complexity is reduced, but adaptability to different object states deteriorates
Solution Approach 1:
The patent transitions from static to dynamic collision determination areas. The area's properties (shape, size, position) are continuously adjusted based on real-time object state data, enabling the simple system structure to adapt to complex scenarios without increasing fundamental device complexity.
Solution Approach 2:
The patent implements adaptability by changing parameters of the collision determination area according to object state. This allows a simple system architecture to handle diverse object scenarios effectively, as the same basic mechanism adapts its parameters rather than requiring complex specialized handling for each state.
Data Source
AI summary
A collision determination area set for a dog as an object in a virtual three-dimensional space is, for example, a sphere having the center at the chest of the dog and having a radius of 30 in the “on all fours” state, a sphere having the center at the hip of the dog and having a radius of 10 in the “standing” state, and a sphere having the center at the head of the dog and having a radius of 10 in the state of the dog “trying to fawn with another dog” in the virtual three-dimensional space. Using the collision determination area set in this manner, it is determined whether or not the dog has collided against another object. Therefore, a collision determination suitable to individual situations can be realized while suppressing an increase in the amount of calculations required for the collision determination.