# 国产阿特拉斯无人机蜂群核心算法Python仿真伪代码
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国产阿特拉斯无人机蜂群核心算法Python仿真伪代码
伪代码说明：贴合蜂群去中心化分布式架构、四大核心作战目标，还原原文五大核心算法逻辑，代码模块化拆分，可直接用于无人机蜂群仿真平台调试、算法验证，无复杂依赖，关键参数均标注原文对应技术指标。
核心仿真前提：
-
单架无人机视为独立智能体，搭载边缘计算单元、自组网通信模块、多源感知模块
-
全局无中心指挥节点，所有运算均为分布式本地计算
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适配无GPS、强电磁干扰场景，支持断联自愈、任务重分配
一、基础常量与无人机智能体类定义
# --------------------------
# 全局仿真常量（原文技术指标）
# --------------------------
DRONE_SAFE_DISTANCE = 5 # 无人机最小安全间距(m)
MAX_CLUSTER_SPEED = 300 # 蜂群最大机动速度(km/h)
TASK_ALLOCATE_TIME = 0.3 # 百机任务分配耗时(s)
GPS_LOST_POS_ACCURACY = 1 # 无GPS定位精度(m)
COMM_DELAY = 0.01 # 自组网通信时延(s)
DAMAGE_LIMIT = 0.2 # 战损阈值(20%)
# 无人机任务类型枚举
TASK_TYPE = {
'RECON': '侦察',
'JAM': '电子干扰',
'ATTACK': '打击',
'ASSESS': '毁伤评估'
}
import math
import random
from collections import defaultdict
# --------------------------
# 无人机智能体类（单机边缘计算单元）
# --------------------------
class DroneAgent:
def __init__(self, drone_id, pos, velocity, payload, battery):
# 基础参数
self.drone_id = drone_id
self.pos = pos # 三维坐标[x,y,z]
self.velocity = velocity # 速度矢量
self.payload = payload # 载荷类型
self.battery = battery # 剩余电量
self.is_alive = True # 存活状态
# 集群协同参数
self.neighbor_drones = [] # 邻机列表（3-5架）
self.current_task = None # 当前任务
self.task_target = None # 任务目标坐标
# 导航参数
self.nav_mode = 'NORMAL' # 正常/无GPS/断联模式
# 获取邻机信息（局部感知，仅获取周边3-5架无人机）
def get_neighbors(self, swarm_list):
self.neighbor_drones = []
for drone in swarm_list:
if drone.drone_id != self.drone_id and self._calc_distance(drone.pos) < 100:
self.neighbor_drones.append(drone)
if len(self.neighbor_drones) >=5:
break
# 计算两架无人机间距
def _calc_distance(self, target_pos):
return math.sqrt(sum([(self.pos[i]-target_pos[i])**2 for i in range(3)]))
二、核心算法1：集群编队控制（Reynolds三规则融合算法）
# --------------------------
# 集群编队控制主函数
# 融合：分离规则+对齐规则+凝聚规则+改进人工势场法
# --------------------------
def cluster_formation_control(drone: DroneAgent, swarm_list, obstacle_list, target_pos):
"""
:param drone: 当前无人机智能体
:param swarm_list: 蜂群全体列表
:param obstacle_list: 战场障碍物/威胁列表
:param target_pos: 任务目标点
:return: 调整后速度矢量（实现编队控制）
"""
# 初始化合加速度
total_acc = [0.0, 0.0, 0.0]
# 1. 分离规则：避免机群碰撞（原文公式实现）
sep_acc = [0.0, 0.0, 0.0]
for neighbor in drone.neighbor_drones:
dist = drone._calc_distance(neighbor.pos)
if dist < DRONE_SAFE_DISTANCE:
# 分离加速度公式
diff = [neighbor.pos[i] - drone.pos[i] for i in range(3)]
norm = dist **2
sep_acc = [sep_acc[i] - diff[i]/norm for i in range(3)]
total_acc = [total_acc[i] + sep_acc[i] for i in range(3)]
# 2. 对齐规则：保持邻机速度航向一致
align_acc = [0.0, 0.0, 0.0]
if drone.neighbor_drones:
avg_vel = [0.0, 0.0, 0.0]
for neighbor in drone.neighbor_drones:
avg_vel = [avg_vel[i] + neighbor.velocity[i] for i in range(3)]
avg_vel = [v/len(drone.neighbor_drones) for v in avg_vel]
align_acc = [avg_vel[i] - drone.velocity[i] for i in range(3)]
total_acc = [total_acc[i] + align_acc[i] for i in range(3)]
# 3. 凝聚规则：向集群中心靠拢
coh_acc = [0.0, 0.0, 0.0]
if drone.neighbor_drones:
center_pos = [0.0, 0.0, 0.0]
for neighbor in drone.neighbor_drones:
center_pos = [center_pos[i] + neighbor.pos[i] for i in range(3)]
center_pos = [p/len(drone.neighbor_drones) for p in center_pos]
coh_acc = [center_pos[i] - drone.pos[i] for i in range(3)]
total_acc = [total_acc[i] + coh_acc[i] for i in range(3)]
# 4. 改进人工势场：目标引力+障碍物斥力
# 目标引力
target_attract = [target_pos[i] - drone.pos[i] for i in range(3)]
total_acc = [total_acc[i] + target_attract[i]*0.2 for i in range(3)]
# 障碍物/防空火力斥力
for obs in obstacle_list:
dist_obs = drone._calc_distance(obs)
if dist_obs < 50:
repel = [drone.pos[i] - obs[i] for i in range(3)]
repel = [repel[i] * (1/dist_obs - 1/50)**2 for i in range(3)]
total_acc = [total_acc[i] + repel[i] for i in range(3)]
# 速度约束，不超过最大机动速度
new_velocity = [drone.velocity[i] + total_acc[i] for i in range(3)]
vel_norm = math.sqrt(sum([v**2 for v in new_velocity]))
if vel_norm > MAX_CLUSTER_SPEED/3.6: # 单位转换m/s
new_velocity = [v/vel_norm * MAX_CLUSTER_SPEED/3.6 for v in new_velocity]
return new_velocity
三、核心算法2：分布式动态任务分配（拍卖共识+强化学习）
# --------------------------
# 动态任务分配主函数
# 分布式拍卖算法，无中心节点，百机计算<0.3s
# --------------------------
def dynamic_task_allocation(swarm_list, target_list):
"""
:param swarm_list: 存活无人机列表
:param target_list: 战场目标列表（坐标+优先级+类型）
:return: 无人机-任务匹配结果
"""
# 任务初始化
task_result = {}
task_pool = defaultdict(list)
# 步骤1：无人机广播自身能力（载荷、电量、航程）
drone_capability = {}
for drone in swarm_list:
if drone.is_alive:
drone_capability[drone.drone_id] = {
'payload': drone.payload,
'battery': drone.battery,
'pos': drone.pos
}
# 步骤2：广播目标价值（优先级、坐标、任务类型）
for idx, target in enumerate(target_list):
target_id = f'TARGET_{idx}'
task_pool[target_id] = {
'pos': target['pos'],
'priority': target['priority'],
'task_type': target['task_type']
}
# 步骤3：分布式自主竞标（收益-成本核算）
bid_result = defaultdict(dict)
for d_id, d_cap in drone_capability.items():
for t_id, t_info in task_pool.items():
# 收益：目标优先级
profit = t_info['priority']
# 成本：航程+载荷匹配度
dist_cost = math.sqrt(sum([(d_cap['pos'][i]-t_info['pos'][i])**2 for i in range(3)]))
payload_cost = 0 if d_cap['payload'] == t_info['task_type'] else 10
total_cost = dist_cost + payload_cost
# 竞标分数
bid_score = profit / total_cost if total_cost !=0 else 0
bid_result[d_id][t_id] = bid_score
# 步骤4：全局共识收敛，最优分配
allocated_target = set()
for d_id, bids in bid_result.items():
if not bids:
continue
# 选取最优竞标目标
best_target = max(bids, key=bids.get)
if best_target not in allocated_target:
allocated_target.add(best_target)
task_result[d_id] = {
'target_id': best_target,
'task_type': task_pool[best_target]['task_type'],
'target_pos': task_pool[best_target]['pos']
}
# 战损补位：未分配无人机待命，随时补位
for drone in swarm_list:
if drone.drone_id not in task_result and drone.is_alive:
task_result[drone.drone_id] = {'task_type': 'STANDBY'}
return task_result
# 战损触发任务重分配
def damage_reallocate(swarm_list, target_list, task_result):
# 筛选存活无人机
alive_swarm = [d for d in swarm_list if d.is_alive]
# 重新执行任务分配
return dynamic_task_allocation(alive_swarm, target_list)
四、核心算法3：自主路径规划（改进蚁群+RRT*）
# --------------------------
# 自主路径规划主函数
# 改进蚁群算法+数字孪生战场，支持无GPS导航
# --------------------------
def path_planning(drone: DroneAgent, target_pos, obstacle_list, gps_status=True):
"""
:param drone: 无人机智能体
:param target_pos: 目标坐标
:param obstacle_list: 威胁/障碍物列表
:param gps_status: GPS是否正常
:return: 最优路径点序列
"""
path = []
current_pos = drone.pos
# 1. GPS正常：数字孪生+蚁群算法全局寻优
if gps_status:
# 改进蚁群算法：多路径并行搜索，规避威胁
pheromone = defaultdict(float)
best_path = []
min_cost = float('inf')
# 迭代寻优（简化仿真逻辑）
for _ in range(50):
temp_path = [current_pos]
current = current_pos
total_cost = 0
while not _is_arrive(current, target_pos):
# 概率选择下一节点
next_pos = _aco_select_next(current, target_pos, obstacle_list, pheromone)
temp_path.append(next_pos)
total_cost += _calc_distance(current, next_pos)
current = next_pos
# 更新最优路径
if total_cost < min_cost:
min_cost = total_cost
best_path = temp_path
# 更新信息素
_update_pheromone(pheromone, temp_path, total_cost)
path = best_path
# 2. GPS丢失：视觉SLAM+惯性导航+地形匹配
else:
# 局部路径规划，定位精度1m
current = current_pos
while not _is_arrive(current, target_pos, threshold=GPS_LOST_POS_ACCURACY):
# 局部避障，惯性导航推进
next_pos = _local_obstacle_avoid(current, target_pos, obstacle_list)
path.append(next_pos)
current = next_pos
return path
# 辅助函数：判断是否抵达目标
def _is_arrive(pos, target_pos, threshold=1):
return math.sqrt(sum([(pos[i]-target_pos[i])**2 for i in range(3)])) < threshold
# 辅助函数：蚁群算法节点选择、信息素更新（简化逻辑）
def _aco_select_next(current, target, obstacles, pheromone):
# 简化仿真，随机选取可行点
candidates = []
for dx in [-10,0,10]:
for dy in [-10,0,10]:
for dz in [-5,0,5]:
new_pos = [current[0]+dx, current[1]+dy, current[2]+dz]
if not _check_collision(new_pos, obstacles):
candidates.append(new_pos)
return random.choice(candidates) if candidates else current
def _update_pheromone(pheromone, path, cost):
for pos in path:
pheromone[tuple(pos)] += 100 / cost
# 局部避障（无GPS模式）
def _local_obstacle_avoid(current, target, obstacles):
# 惯性导航+视觉感知，小幅调整规避障碍
direct = [target[i]-current[i] for i in range(3)]
new_pos = [current[i] + direct[i]*0.1 for i in range(3)]
if _check_collision(new_pos, obstacles):
new_pos = [current[0]+random.choice([-5,5]), current[1], current[2]]
return new_pos
def _check_collision(pos, obstacles):
for obs in obstacles:
if math.sqrt(sum([(pos[i]-obs[i])**2 for i in range(3)])) < 20:
return True
return False
def _calc_distance(pos1, pos2):
return math.sqrt(sum([(pos1[i]-pos2[i])**2 for i in range(3)]))
五、蜂群仿真主函数（全流程闭环）
# --------------------------
# 蜂群全流程作战仿真主函数
# 贴合原文实战作战流程：发射-组网-巡航-打击-重组-返航
# --------------------------
def drone_swarm_simulation():
# 1. 初始化蜂群（96架，模块化部署）
swarm_list = []
for i in range(96):
pos = [random.randint(0,100), random.randint(0,100), random.randint(50,100)]
velocity = [0, 10, 0]
payload = random.choice(['RECON', 'JAM', 'ATTACK', 'ASSESS'])
swarm_list.append(DroneAgent(i, pos, velocity, payload, battery=100))
# 2. 战场初始化
obstacle_list = [[200,200,80], [300,150,70]] # 防空火力/地形障碍
target_list = [ # 作战目标
{'pos': [500,500,60], 'priority': 10, 'task_type': 'ATTACK'},
{'pos': [450,550,60], 'priority': 8, 'task_type': 'RECON'},
{'pos': [550,450,60], 'priority': 9, 'task_type': 'JAM'}
]
# 3. 蜂群组网+邻机感知
for drone in swarm_list:
drone.get_neighbors(swarm_list)
# 4. 任务分配
task_result = dynamic_task_allocation(swarm_list, target_list)
# 绑定任务至无人机
for drone in swarm_list:
if drone.drone_id in task_result:
drone.current_task = task_result[drone.drone_id]['task_type']
drone.task_target = task_result[drone.drone_id].get('target_pos', None)
# 5. 编队巡航+路径规划
for drone in swarm_list:
if drone.is_alive and drone.task_target:
# 编队控制
new_vel = cluster_formation_control(drone, swarm_list, obstacle_list, drone.task_target)
drone.velocity = new_vel
# 路径规划
drone_path = path_planning(drone, drone.task_target, obstacle_list)
# 6. 模拟战损，触发任务重分配
# 随机击毁20%无人机
damage_num = int(len(swarm_list) * DAMAGE_LIMIT)
damaged_drones = random.sample(swarm_list, damage_num)
for d in damaged_drones:
d.is_alive = False
# 战损补位
new_task_result = damage_reallocate(swarm_list, target_list, task_result)
# 7. 饱和打击+毁伤评估（简化）
print("蜂群任务分配完成，进入饱和打击阶段，多角度同步命中...")
print("战损自愈完成，剩余无人机继续执行任务，作战效能保留90%+")
# 8. 任务完成，自主返航
print("作战任务完成，蜂群自主返航/待机")
# 启动仿真
if __name__ == '__main__':
drone_swarm_simulation()
伪代码使用说明
-
仿真适配：可直接对接PyBullet、ROS等无人机仿真平台，替换底层坐标、通信接口即可运行
-
参数调整：直接修改顶部常量，适配不同集群规模、作战环境、机动指标
-
算法扩展：可在对应函数内加入强化学习（DQN/PPO）、目标识别AI模型，贴合原文完整算法体系
-
工程落地：剥离仿真逻辑后，可移植至机载边缘芯片，适配真实无人机蜂群硬件
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