Final answer:
Collision detection for rectangular objects often uses AABB testing, which evaluates overlaps on each axis, rather than directly applying distances such as Euclidean or Manhattan distances.
Step-by-step explanation:
When computing collision detection between two objects that are approximately rectangular in shape, we generally employ an Axis-Aligned Bounding Box (AABB) for efficient and straightforward computations. This method checks whether the two-dimensional (2D) or three-dimensional (3D) boxes that surround each object overlap on all axes. However, the specific distances mentioned, such as Euclidean, Manhattan, Circular, and Octagonal distances, serve different purposes and are not typically used directly for collision detection between rectangular objects.
In collision detection:
- Euclidean distance calculates the shortest straight line distance between two points in space.
- Manhattan distance measures the distance between two points in a grid based on a strictly horizontal and/or vertical path.
- Circular distance might refer to the distance around the circumference of a circle between two points.
- Octagonal distance is not a standard term used in geometry or collision detection.
For axis-aligned rectangles, a simple comparison of the objects' bounding coordinates is often sufficient to determine if a collision occurred. This approach is more aligned with AABB testing, where overlaps on the X and Y (and Z if 3D) axes are evaluated.