python实现gjk算法

GJK算法是一种用于计算两个凸多边形之间最小距离的算法，其基本思想是通过迭代逼近两个凸多边形的Minkowski差集的最小点，从而得到最小距离。下面是一个简单的Python实现：

import numpy as np

def support(a, b, d):
    return np.argmax(np.dot(a, d)), np.argmax(np.dot(b, -d))

def gjk(a, b):
    d = np.array([1, 0])
    p = support(a, b, d)
    q = support(a, b, -d)
    d = q - p
    simplex = [q, p]
    while True:
        a, b = simplex[-1], simplex[-2]
        ao, bo = -a, b
        ab = b - a
        ap = support(a, b, ab)
        if np.dot(ap - a, ab) <= 0:
            return False
        bp = support(a, b, -ab)
        if np.dot(bp - b, -ab) <= 0:
            return False
        if np.dot(ap - a, ao) >= 0 and np.dot(bp - b, bo) >= 0:
            return True
        if np.dot(ap - a, ao) < np.dot(bp - b, bo):
            simplex[-2], simplex[-1] = simplex[-1], simplex[-2]
            simplex[-1] = ap
            d = np.cross(ab, ap - a)
        else:
            simplex[-2], simplex[-1] = simplex[-1], simplex[-2]
            simplex[-1] = bp
            d = np.cross(ab, bp - b)

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