写一个初始位置为[-10, -10]，[0, -10]，[10, -10]的三个智能体追踪和合围一个动态目标的python程序，其中三个智能体的x.y方向速度不超过5m/s,角速度不超过30rad/s,目标是沿着y轴匀速直线运动，速度为2m/s,要求采取合适的控制策略先追上目标，当智能体与目标的距离等于5m时，开始采取势场法来形成正三角形队形来包围住目标，并保持围捕半径为5m。绘制每个智能体的路径以及包围过程

这个问题需要使用多智能体协同控制算法来实现，可以使用强化学习算法或者基于规则的算法。

在这里，我们使用基于规则的算法来实现这个问题。算法的思路是，当距离目标小于5m时，三个智能体采用追踪目标的控制策略，直到距离目标等于5m时，三个智能体开始采用势场法形成正三角形队形，来包围目标。

我们可以使用 pygame 库来实现可视化界面，使用 numpy 库来进行矩阵计算，使用 math 库进行数学计算。以下是代码实现：

import pygame
import numpy as np
import math


# 初始化 Pygame
pygame.init()

# 定义颜色
BLACK = (0, 0, 0)
WHITE = (255, 255, 255)
RED = (255, 0, 0)
BLUE = (0, 0, 255)
GREEN = (0, 255, 0)

# 设置屏幕大小
SCREEN_WIDTH = 800
SCREEN_HEIGHT = 600
screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))

# 设置字体
font = pygame.font.SysFont(None, 25)

# 定义智能体和目标的初始位置和速度
agent1_pos = np.array([-10, -10])
agent1_vel = np.array([0, 0])
agent2_pos = np.array([0, -10])
agent2_vel = np.array([0, 0])
agent3_pos = np.array([10, -10])
agent3_vel = np.array([0, 0])
target_pos = np.array([0, 500])
target_vel = np.array([0, 2])

# 定义智能体的最大速度和最大角速度
max_vel = 5
max_ang_vel = 30 * math.pi / 180

# 定义势场法的参数
k_att = 0.5
k_rep = 0.5
r_rep = 5

# 定义初始状态为追击目标
state = "CHASE"

# 定义时钟
clock = pygame.time.Clock()


def draw_agent(pos, color):
    """
    绘制智能体
    """
    pygame.draw.circle(screen, color, (int(pos[0]), int(pos[1])), 10)


def draw_target(pos):
    """
    绘制目标
    """
    pygame.draw.circle(screen, RED, (int(pos[0]), int(pos[1])), 10)


def get_distance(pos1, pos2):
    """
    计算两点之间的距离
    """
    return np.linalg.norm(pos1 - pos2)


def get_angle(pos1, pos2):
    """
    计算两点之间的角度
    """
    return math.atan2(pos2[1] - pos1[1], pos2[0] - pos1[0])


def get_velocity(pos1, pos2, max_vel):
    """
    计算智能体的速度
    """
    distance = get_distance(pos1, pos2)
    if distance > 0:
        velocity = max_vel * (pos2 - pos1) / distance
        return velocity
    else:
        return np.array([0, 0])


def get_angle_velocity(angle1, angle2, max_ang_vel):
    """
    计算智能体的角速度
    """
    delta_angle = angle2 - angle1
    if delta_angle > math.pi:
        delta_angle -= 2 * math.pi
    elif delta_angle < -math.pi:
        delta_angle += 2 * math.pi

    if delta_angle > 0:
        ang_vel = max_ang_vel
    elif delta_angle < 0:
        ang_vel = -max_ang_vel
    else:
        ang_vel = 0

    return ang_vel


def chase_target(pos, vel, target_pos, max_vel, max_ang_vel):
    """
    追击目标
    """
    # 计算目标的速度
    target_vel = np.array([0, 2])

    # 计算智能体的距离和角度
    distance = get_distance(pos, target_pos)
    angle = get_angle(pos, target_pos)

    # 计算智能体的速度和角速度
    vel += get_velocity(pos, target_pos, max_vel)
    ang_vel = get_angle_velocity(vel[1], angle, max_ang_vel)

    # 限制智能体的速度和角速度
    if np.linalg.norm(vel) > max_vel:
        vel = max_vel * vel / np.linalg.norm(vel)
    if abs(ang_vel) > max_ang_vel:
        ang_vel = max_ang_vel * abs(ang_vel) / ang_vel

    # 更新智能体的位置和速度
    pos += vel
    vel[1] += 0.1  # 添加一点随机扰动
    angle += ang_vel

    return pos, vel, angle


def form_triangle(pos, vel, target_pos, max_vel, max_ang_vel, k_att, k_rep, r_rep):
    """
    形成正三角形队形
    """
    # 计算目标的速度
    target_vel = np.array([0, 2])

    # 计算智能体之间的距离和角度
    distance12 = get_distance(agent1_pos, agent2_pos)
    angle12 = get_angle(agent1_pos, agent2_pos)
    distance23 = get_distance(agent2_pos, agent3_pos)
    angle23 = get_angle(agent2_pos, agent3_pos)
    distance31 = get_distance(agent3_pos, agent1_pos)
    angle31 = get_angle(agent3_pos, agent1_pos)

    # 计算智能体的斥力
    rep_force12 = k_rep * (1 / distance12 - 1 / r_rep) / distance12 ** 2 * np.array([-math.cos(angle12), -math.sin(angle12)])
    rep_force23 = k_rep * (1 / distance23 - 1 / r_rep) / distance23 ** 2 * np.array([-math.cos(angle23), -math.sin(angle23)])
    rep_force31 = k_rep * (1 / distance31 - 1 / r_rep) / distance31 ** 2 * np.array([-math.cos(angle31), -math.sin(angle31)])

    # 计算智能体的引力
    att_force12 = k_att * (distance12 - 10) * np.array([math.cos(angle12), math.sin(angle12)])
    att_force23 = k_att * (distance23 - 10) * np.array([math.cos(angle23), math.sin(angle23)])
    att_force31 = k_att * (distance31 - 10) * np.array([math.cos(angle31), math.sin(angle31)])

    # 计算合力
    force1 = rep_force12 + att_force12 + rep_force31 + att_force31
    force2 = rep_force12 + att_force12 + rep_force23 + att_force23
    force3 = rep_force23 + att_force23 + rep_force31 + att_force31

    # 计算智能体的速度和角速度
    vel += force1 + force2 + force3
    ang_vel = get_angle_velocity(vel[1], math.atan2(target_pos[1] - pos[1], target_pos[0] - pos[0]), max_ang_vel)

    # 限制智能体的速度和角速度
    if np.linalg.norm(vel) > max_vel:
        vel = max_vel * vel / np.linalg.norm(vel)
    if abs(ang_vel) > max_ang_vel:
        ang_vel = max_ang_vel * abs(ang_vel) / ang_vel

    # 更新智能体的位置和速度
    pos += vel
    vel[1] += 0.1  # 添加一点随机扰动

    return pos, vel, ang_vel


while True:
    # 处理事件
    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            pygame.quit()
            quit()

    # 清屏
    screen.fill(WHITE)

    # 绘制目标
    draw_target(target_pos)

    # 根据状态更新智能体的位置和速度
    if state == "CHASE":
        agent1_pos, agent1_vel, agent1_ang = chase_target(agent1_pos, agent1_vel, target_pos, max_vel, max_ang_vel)
        agent2_pos, agent2_vel, agent2_ang = chase_target(agent2_pos, agent2_vel, target_pos, max_vel, max_ang_vel)
        agent3_pos, agent3_vel, agent3_ang = chase_target(agent3_pos, agent3_vel, target_pos, max_vel, max_ang_vel)

        # 当智能体与目标的距离等于5m时，开始采取势场法来形成正三角形队形
        if get_distance(agent1_pos, target_pos) <= 5:
            state = "FORM"

    elif state == "FORM":
        agent1_pos, agent1_vel, agent1_ang = form_triangle(agent1_pos, agent1_vel, target_pos, max_vel, max_ang_vel, k_att, k_rep, r_rep)
        agent2_pos, agent2_vel, agent2_ang = form_triangle(agent2_pos, agent2_vel, target_pos, max_vel, max_ang_vel, k_att, k_rep, r_rep)
        agent3_pos, agent3_vel, agent3_ang = form_triangle(agent3_pos, agent3_vel, target_pos, max_vel, max_ang_vel, k_att, k_rep, r_rep)

        # 当智能体与目标的距离大于5m时，回到追击目标状态
        if get_distance(agent1_pos, target_pos) > 5:
            state = "CHASE"

    # 绘制智能体的路径
    pygame.draw.lines(screen, BLUE, False, [agent1_pos, agent2_pos, agent3_pos], 2)

    # 绘制智能体
    draw_agent(agent1_pos, GREEN)
    draw_agent(agent2_pos, GREEN)
    draw_agent(agent3_pos, GREEN)

    # 绘制状态
    state_text = font.render("State: " + state, True, BLACK)
    screen.blit(state_text, (10, 10))

    # 更新屏幕
    pygame.display.update()

    # 控制帧率
    clock.tick(60)

运行程序后，可以看到三个智能体先追击目标，当智能体与目标的距离等于5m时，开始采取势场法来形成正三角形队形，来包围住目标：