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Game Physics Cookbook

Game Physics Cookbook

By : Gabor Szauer
4.3 (4)
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Game Physics Cookbook

Game Physics Cookbook

4.3 (4)
By: Gabor Szauer

Overview of this book

Physics is really important for game programmers who want to add realism and functionality to their games. Collision detection in particular is a problem that affects all game developers, regardless of the platform, engine, or toolkit they use. This book will teach you the concepts and formulas behind collision detection. You will also be taught how to build a simple physics engine, where Rigid Body physics is the main focus, and learn about intersection algorithms for primitive shapes. You’ll begin by building a strong foundation in mathematics that will be used throughout the book. We’ll guide you through implementing 2D and 3D primitives and show you how to perform effective collision tests for them. We then pivot to one of the harder areas of game development—collision detection and resolution. Further on, you will learn what a Physics engine is, how to set up a game window, and how to implement rendering. We’ll explore advanced physics topics such as constraint solving. You’ll also find out how to implement a rudimentary physics engine, which you can use to build an Angry Birds type of game or a more advanced game. By the end of the book, you will have implemented all primitive and some advanced collision tests, and you will be able to read on geometry and linear Algebra formulas to take forward to your own games!
Table of Contents (19 chapters)
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18
Index

Generic collisions

A large part of this book was dedicated to finding the most efficient way of determining whether two shapes intersect. The most robust, general purpose algorithm we have talked about so far has been Separating Axis Theorem (SAT). SAT has several limitations, the biggest one being curved surfaces. The execution time of SAT also gets out of hand when a complex mesh has many faces.

In this section, we will discuss a different generic algorithm--the Gilbert Johnson Keerthi or GJK algorithm. GJK runs in near linear time, often outperforming SAT. However, it is difficult to achieve the stability SAT provides using GJK. GJK should be used to find intersection data with complex meshes that have many faces. The GJK algorithm needs a support function to work, and this support function is called Minkowski Sum.

Note

For an algorithm to run in linear time, adding an iteration increases the execution time of the algorithm by the same amount every time, regardless of the size of the dataset...

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