最后通过u,v,w的正负来判断query point到底属于哪个region中: GJK的算法证明很难,但我们可以通过一个例子来对其原理有一定理解: GJK algorithm Input: polygon and point Q pick arbitrary initial simplex S loop compute closest point P on S cull non-contributing vertices from S build vector d pointing fro...
对于GetClosestPoint函数,获取一个单纯形最近的点。 对于GetWitnessPoints函数,主要获取两形状之间的见证点,所谓见证点,就是形成一个单纯形顶点的两个形状上的顶点。 对于GetMetric函数,我们将获取单纯形的距离或面积,作为判断是否需要更新缓存的一个条件。 寻找原点位置的函数 有两个,solve2与solve3,用来更新单纯形...
d=c2-c1;// 和GJK碰撞检测类似Simplex.add(support(shape1,shape2,d));// Simplex 中加入 a 点Simplex.add(support(shape1,shape2,-d));// Simplex 中加入 b 点// 从原点指向 ab 线段上距离原点最近的点的向量, 例如恰好就是答案的话, 则 d.magnitude() 就是答案d=ClosestPointToOrigin(Simplex.a,...
由support函数可知,需要一个direction来选择Minkowski Sum点,任意的direction都是可以的,但是选择两个多边形的中心点的向量方向是较优的选择。 Vector d = // choose a search direction// get the first Minkowski Difference pointSimplex.add(support(A, B, d));// negate d for the next pointd.negate();...
（转）GJK算法详细介绍 （转）GJK算法详细介绍 1. 概述 2. 凸体(凸多⾯体或凸多边形)3. 明可夫斯基和(Minkowski Sum)4. 单纯形(Simplex)5. Support函数 6. 创建单纯形 7. 判定碰撞 8. 迭代 9. 检测单纯形 概述 和SAT(分离轴定理)算法⼀样，GJK算法也只对凸体有效。 GJK算法的优势是：通过support...
01 publicPoint support(Shape shape1, Shape shape2, Vector d) { 02// d is a vector direction (doesn't have to be normalized) 03// get points on the edge of the shapes in opposite directions 04Point p1 = shape1.getFarthestPointInDirection(d); ...
01 publicPoint support(Shape shape1, Shape shape2, Vector d) { 02// d is a vector direction (doesn't have to be normalized) 03// get points on the edge of the shapes in opposite directions 04Point p1 = shape1.getFarthestPointInDirection(d); ...
Point p2=shape2.getFarthestPointInDirection(d.negative());//perform the Minkowski DifferencePoint p3 =p1.subtract(p2);//p3 is now a point in Minkowski space on the edge of the Minkowski Differencereturnp3; } 下面这个例子使用图2的物体形状,执行函数三次 ...
Point p2=shape2.getFarthestPointInDirection(d.negative());//perform the Minkowski DifferencePoint p3 =p1.subtract(p2);//p3 is now a point in Minkowski space on the edge of the Minkowski Differencereturnp3; } 下面这个例子使用图2的物体形状,执行函数三次 ...
# add new point to simplex i = find_first_unset(bitset) bitset = set_bit(bitset, i) S[:, i] = w S1[:, i] = a S2[:, i] = b # compute closest point to the origin in the simplex, as well the # smallest simplex that supports that point ...