183 lines
6.0 KiB
Python
183 lines
6.0 KiB
Python
"""
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高级群体智能示例
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演示使用高级Boids算法和优化功能
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"""
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from panda3d.core import Vec3
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import random
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import time
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def create_optimized_swarm(swarm_manager):
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"""
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创建优化的群体
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使用空间分区和高级行为
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"""
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print("创建优化的群体...")
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# 配置高级参数
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swarm_manager.config.set("member_count", 200) # 增加群体规模
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swarm_manager.config.set("cohesion_weight", 1.2)
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swarm_manager.config.set("separation_weight", 1.8)
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swarm_manager.config.set("alignment_weight", 1.0)
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swarm_manager.config.set("perception_radius", 15.0)
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swarm_manager.config.set("max_speed", 8.0)
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# 启用物理效果
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swarm_manager.config.set("gravity_enabled", True)
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swarm_manager.config.set("air_resistance_enabled", True)
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# 启用空间分区优化
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swarm_manager.config.set("spatial_partitioning", True)
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swarm_manager.config.set("neighbor_search_optimization", True)
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# 重新创建群体
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swarm_manager.reset()
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print(f"优化群体创建完成，包含{len(swarm_manager.members)}个成员")
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def create_complex_obstacles(swarm_manager, count=20):
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"""
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创建复杂障碍物环境
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"""
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print(f"创建{count}个复杂障碍物...")
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for i in range(count):
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# 随机位置
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position = Vec3(
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random.uniform(-40, 40),
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random.uniform(-40, 40),
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random.uniform(5, 25)
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)
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# 随机半径
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radius = random.uniform(1, 8)
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# 创建障碍物
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swarm_manager.create_obstacle(position, radius)
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print(f"复杂障碍物创建完成")
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def create_hierarchical_flock(swarm_manager):
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"""
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创建分层群体结构
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包含领导者和跟随者
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"""
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print("创建分层群体结构...")
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# 标记前10%的成员为领导者
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leader_count = max(1, len(swarm_manager.members) // 10)
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for i, member in enumerate(swarm_manager.members):
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if i < leader_count:
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member['is_leader'] = True
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# 设置领导者颜色
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if 'node' in member:
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member['node'].setColor(0, 1, 0, 1) # 绿色
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else:
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member['is_leader'] = False
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# 设置跟随者颜色
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if 'node' in member:
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member['node'].setColor(1, 1, 1, 1) # 白色
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# 调整领导者参数
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swarm_manager.config.set("leader_cohesion_weight", 2.0)
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swarm_manager.config.set("leader_separation_weight", 1.0)
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print(f"分层群体结构创建完成，{leader_count}个领导者")
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def create_dynamic_environment(swarm_manager):
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"""
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创建动态环境
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包括移动障碍物和变化的目标
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"""
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print("创建动态环境...")
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# 添加移动障碍物
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moving_obstacle = {
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'position': Vec3(0, 0, 15),
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'radius': 5.0,
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'velocity': Vec3(2, 1, 0),
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'bounds': {'min_x': -30, 'max_x': 30, 'min_y': -30, 'max_y': 30}
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}
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swarm_manager.moving_obstacles = [moving_obstacle]
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# 设置移动目标
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swarm_manager.set_target(Vec3(20, 20, 15))
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swarm_manager.config.set("seek_enabled", True)
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print("动态环境创建完成")
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def update_dynamic_environment(swarm_manager, dt):
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"""
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更新动态环境
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移动障碍物和变化目标
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"""
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# 更新移动障碍物
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if hasattr(swarm_manager, 'moving_obstacles'):
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for obstacle in swarm_manager.moving_obstacles:
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# 移动障碍物
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obstacle['position'] += obstacle['velocity'] * dt
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# 边界反弹
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bounds = obstacle['bounds']
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if obstacle['position'].x < bounds['min_x'] or obstacle['position'].x > bounds['max_x']:
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obstacle['velocity'].x *= -1
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if obstacle['position'].y < bounds['min_y'] or obstacle['position'].y > bounds['max_y']:
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obstacle['velocity'].y *= -1
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# 保持在边界内
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obstacle['position'].x = max(bounds['min_x'], min(bounds['max_x'], obstacle['position'].x))
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obstacle['position'].y = max(bounds['min_y'], min(bounds['max_y'], obstacle['position'].y))
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# 定期改变目标位置
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if not hasattr(swarm_manager, 'last_target_change'):
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swarm_manager.last_target_change = time.time()
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if time.time() - swarm_manager.last_target_change > 10.0: # 每10秒改变一次目标
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new_target = Vec3(
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random.uniform(-30, 30),
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random.uniform(-30, 30),
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random.uniform(10, 25)
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)
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swarm_manager.set_target(new_target)
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swarm_manager.last_target_change = time.time()
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def create_performance_test(swarm_manager):
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"""
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创建性能测试场景
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"""
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print("创建性能测试场景...")
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# 创建大规模群体
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swarm_manager.config.set("member_count", 500)
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swarm_manager.reset()
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# 添加大量障碍物
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create_complex_obstacles(swarm_manager, 50)
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# 启用所有优化
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swarm_manager.config.set("spatial_partitioning", True)
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swarm_manager.config.set("neighbor_search_optimization", True)
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swarm_manager.config.set("gravity_enabled", True)
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swarm_manager.config.set("air_resistance_enabled", True)
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print("性能测试场景创建完成")
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def run_advanced_example(swarm_manager):
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"""
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运行高级示例
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"""
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print("=== 高级群体智能示例 ===")
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# 创建优化群体
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create_optimized_swarm(swarm_manager)
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# 创建复杂环境
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create_complex_obstacles(swarm_manager, 10)
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create_hierarchical_flock(swarm_manager)
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create_dynamic_environment(swarm_manager)
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print("高级示例设置完成")
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print("观察群体的复杂行为和优化性能")
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return update_dynamic_environment |