def uav_update(time_counter: int, p: parameter.Parameter,
uav_swarm: uav.Uav_Swarm, target_swarm: target.Target_Swarm):
p.S_a_p = p.S_a
p.S_r_p = p.S_r
p.V = np.zeros([p.nx, p.ny], dtype=float)
for i in range(p.nu):
[pos_nox_x, pos_nox_y] = uav_swarm[i].pos_now
p.detect_map[pos_nox_x, pos_nox_y] = 1
p.V[pos_nox_x, pos_nox_y] = 1
# 更新单无人机信息
# uav_swarm[i] = uav_single_step(time_counter, p, uav_swarm[i], target_swarm)
uav_swarm[i].pos_past = uav_swarm[i].pos_now
uav_swarm[i].path[time_counter] = uav_swarm[i].pos_now
# 改变位置
uav_swarm[i].pos_now = uav_swarm[i].way_global[0] # 更新当前位置
# 更新概率图 arm, target_swarm)
[pos_nox_x, pos_nox_y] = uav_swarm[i].pos_now
t_map_num = p.t_map[pos_nox_x, pos_nox_y]
if t_map_num != -1: # 遇到目标
target_swarm[t_map_num].found_flag = True
for j in range(p.nt):
target_swarm[j].p_map[pos_nox_x, pos_nox_y] = 0
# 除法还未加入对全0的考虑，或许也可以不修改概率地图
p.found_counter = 0
for i in range(p.nt):
p_map_sum = np.sum(target_swarm[i].p_map)
if not target_swarm[i].found_flag and p_map_sum != 0:
target_swarm[i].p_map = target_swarm[i].p_map / p_map_sum
elif target_swarm[i].found_flag:
p.found_counter += target_swarm[i].found_flag
def uav_swarm_step(time_counter: int, p: parameter.Parameter,
uav_swarm: uav.Uav_Swarm,
target_swarm: target.Target_Swarm):
# 求下一步策略
p.S_a_p = p.S_a
p.S_a = cal_S_a(p)
p.S_r_p = p.S_r
p.S_r = cal_S_r(p)
search_way_local(p, uav_swarm, target_swarm)
for i in range(p.nu):
search_way_global(time_counter, p, uav_swarm[i], uav_swarm,
target_swarm)
optimal.interation(time_counter, uav_swarm, target_swarm, p)