智能导航：CANN加速的实时3D路径规划系统

# requirements_path_planning.txt
torch>=2.0.0
torchvision>=0.15.0
torch_geometric>=2.3.0
numpy>=1.24.0
opencv-python>=4.8.0
scipy>=1.10.0
networkx>=3.0
pygame>=2.5.0  # 可视化
matplotlib>=3.7.0

# 3D环境处理
open3d>=0.17.0
trimesh>=3.23.0
pclpy>=0.13.0  # 点云库

# 路径规划专用
nav_msgs>=1.13.0  # 导航消息
tf2_ros>=0.7.0    # 坐标变换

# CANN相关
aclruntime>=0.2.0
torch_npu>=2.0.0

class EnvironmentEncoderCANN:
    """基于CANN的3D环境编码器"""
    
    def __init__(self, 
                 model_path: str = "models/environment_encoder_cann"):
        self.model_path = model_path
        
        # 3D卷积编码器
        self.conv3d_encoder = nn.Sequential(
            nn.Conv3d(1, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool3d(2),
            nn.Conv3d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool3d(2),
            nn.Conv3d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU()
        )
        
        # 点云特征提取
        self.pointnet_encoder = PointNetEncoder()
        
        # 语义分割网络
        self.semantic_seg = SemanticSegmentationNet()
        
        # 动态障碍物检测
        self.obstacle_detector = ObstacleDetector()
        
        # CANN加速模块
        self.cann_processor = EnvironmentCANN()
        
        print("[INFO] 3D环境编码器初始化完成")
    
    def encode_environment(self,
                          environment_data: Dict,
                          use_cann: bool = True) -> Dict:
        """编码3D环境信息"""
        
        # 提取环境数据
        point_cloud = environment_data.get('point_cloud')
        occupancy_grid = environment_data.get('occupancy_grid')
        semantic_map = environment_data.get('semantic_map')
        
        encoded_features = {}
        
        # 1. 点云特征提取
        if point_cloud is not None:
            if use_cann and self.cann_processor is not None:
                point_features = self.cann_processor.encode_point_cloud(
                    point_cloud
                )
            else:
                point_features = self.pointnet_encoder(point_cloud)
            encoded_features['point_features'] = point_features
        
        # 2. 占据栅格编码
        if occupancy_grid is not None:
            # 转换为3D张量 [batch, channels, depth, height, width]
            grid_tensor = torch.tensor(occupancy_grid).unsqueeze(0).unsqueeze(0)
            
            if use_cann:
                grid_features = self.cann_processor.encode_occupancy_grid(
                    grid_tensor
                )
            else:
                grid_features = self.conv3d_encoder(grid_tensor)
            encoded_features['grid_features'] = grid_features
        
        # 3. 语义特征提取
        if semantic_map is not None:
            semantic_features = self.semantic_seg(semantic_map)
            encoded_features['semantic_features'] = semantic_features
        
        # 4. 动态障碍物检测
        dynamic_obstacles = self.obstacle_detector.detect(
            environment_data.get('sensor_data', {})
        )
        encoded_features['dynamic_obstacles'] = dynamic_obstacles
        
        # 5. 融合所有特征
        fused_encoding = self._fuse_features(encoded_features)
        
        # 6. 构建代价地图
        cost_map = self._build_cost_map(fused_encoding, environment_data)
        
        return {
            'environment_encoding': fused_encoding,
            'cost_map': cost_map,
            'obstacles': dynamic_obstacles,
            'feature_maps': encoded_features
        }
    
    def _build_cost_map(self,
                       encoding: torch.Tensor,
                       environment_data: Dict) -> np.ndarray:
        """构建代价地图"""
        
        # 基础代价（距离、障碍物等）
        base_cost = self._compute_base_cost(encoding)
        
        # 动态代价（交通、天气等）
        dynamic_cost = self._compute_dynamic_cost(
            environment_data.get('dynamic_factors', {})
        )
        
        # 语义代价（道路类型、优先级等）
        semantic_cost = self._compute_semantic_cost(
            environment_data.get('semantic_info', {})
        )
        
        # 组合代价
        cost_map = base_cost + dynamic_cost + semantic_cost
        
        # 归一化
        cost_map = (cost_map - cost_map.min()) / (cost_map.max() - cost_map.min() + 1e-8)
        
        return cost_map
    
    def _compute_base_cost(self, encoding: torch.Tensor) -> np.ndarray:
        """计算基础代价"""
        # 基于编码特征的代价计算
        cost = np.zeros((encoding.shape[2], encoding.shape[3]))
        
        # 简单的距离变换（可替换为更复杂的模型）
        for i in range(encoding.shape[2]):
            for j in range(encoding.shape[3]):
                # 障碍物代价
                obstacle_cost = encoding[0, 0, i, j].item() * 10.0
                
                # 坡度代价
                slope_cost = encoding[0, 1, i, j].item() * 5.0
                
                # 路面状况代价
                surface_cost = encoding[0, 2, i, j].item() * 3.0
                
                cost[i, j] = obstacle_cost + slope_cost + surface_cost
        
        return cost

class PathGeneratorCANN:
    """基于CANN的路径生成模型"""
    
    def __init__(self,
                 model_path: str,
                 device_id: int = 0):
        
        self.model_path = model_path
        self.device_id = device_id
        
        # 路径规划参数
        self.max_path_length = 1000  # 最大路径点数量
        self.resolution = 0.1        # 地图分辨率（米）
        
        # 初始化CANN环境
        self._init_cann()
        
        # 注意力路径生成器
        self.path_generator = AttentionPathGenerator()
        
        # 轨迹优化器
        self.trajectory_optimizer = TrajectoryOptimizer()
        
        print("[INFO] 路径生成模型CANN初始化完成")
    
    def _init_cann(self):
        """初始化CANN路径规划环境"""
        ret = acl.init()
        self._check_ret(ret, "ACL初始化")
        
        ret = acl.rt.set_device(self.device_id)
        self._check_ret(ret, "设置设备")
        
        # 创建上下文
        self.context, ret = acl.rt.create_context(self.device_id)
        self._check_ret(ret, "创建上下文")
        
        # 加载模型
        self.model_id, ret = acl.mdl.load_from_file(self.model_path)
        self._check_ret(ret, "加载模型")
        
        # 准备缓冲区
        self._prepare_path_buffers()
        
        # 创建专用流
        self.stream, ret = acl.rt.create_stream()
        self._check_ret(ret, "创建流")
    
    def _prepare_path_buffers(self):
        """准备路径数据缓冲区"""
        # 代价地图缓冲区
        cost_map_size = 1000 * 1000 * 4  # 假设1000x1000地图
        self.cost_buffer = acl.rt.malloc(
            cost_map_size, acl.mem.malloc_type.DEVICE
        )
        
        # 起点终点缓冲区
        self.start_goal_buffer = acl.rt.malloc(
            6 * 4,  # 3D起点 + 3D终点
            acl.mem.malloc_type.DEVICE
        )
        
        # 输出路径缓冲区
        self.path_buffer = acl.rt.malloc(
            self.max_path_length * 3 * 4,  # 3D路径点
            acl.mem.malloc_type.DEVICE
        )
    
    def plan_path(self,
                 start_point: np.ndarray,
                 goal_point: np.ndarray,
                 environment_encoding: Dict,
                 constraints: Optional[Dict] = None) -> Dict:
        """规划路径"""
        
        start_time = time.time()
        
        print(f"开始路径规划: {start_point} -> {goal_point}")
        
        # 1. 提取代价地图
        cost_map = environment_encoding['cost_map']
        
        # 2. 应用约束条件
        if constraints is not None:
            cost_map = self._apply_constraints(cost_map, constraints)
        
        # 3. CANN加速的路径生成
        if self.cann_processor is not None:
            raw_path = self._cann_path_generation(
                cost_map, start_point, goal_point
            )
        else:
            raw_path = self._cpu_path_generation(
                cost_map, start_point, goal_point
            )
        
        # 4. 路径平滑与优化
        smoothed_path = self.trajectory_optimizer.optimize(raw_path)
        
        # 5. 路径可行性检查
        feasible_path = self._check_feasibility(
            smoothed_path, environment_encoding
        )
        
        # 6. 生成轨迹（速度、加速度剖面）
        trajectory = self._generate_trajectory(feasible_path, constraints)
        
        planning_time = time.time() - start_time
        
        print(f"路径规划完成，耗时: {planning_time*1000:.2f}ms")
        print(f"路径长度: {len(feasible_path)}个点")
        print(f"路径代价: {self._compute_path_cost(feasible_path, cost_map):.3f}")
        
        return {
            'raw_path': raw_path,
            'smoothed_path': smoothed_path,
            'feasible_path': feasible_path,
            'trajectory': trajectory,
            'planning_time': planning_time,
            'path_length': len(feasible_path),
            'path_cost': self._compute_path_cost(feasible_path, cost_map)
        }
    
    def _cann_path_generation(self,
                             cost_map: np.ndarray,
                             start_point: np.ndarray,
                             goal_point: np.ndarray) -> np.ndarray:
        """CANN加速的路径生成"""
        
        # 准备输入数据
        cost_data = cost_map.flatten().astype(np.float32)
        start_goal_data = np.concatenate([start_point, goal_point]).astype(np.float32)
        
        # 复制数据到设备
        acl.rt.memcpy(self.cost_buffer,
                     cost_data.nbytes,
                     cost_data.ctypes.data,
                     cost_data.nbytes,
                     acl.rt.memcpy_kind.HOST_TO_DEVICE)
        
        acl.rt.memcpy(self.start_goal_buffer,
                     start_goal_data.nbytes,
                     start_goal_data.ctypes.data,
                     start_goal_data.nbytes,
                     acl.rt.memcpy_kind.HOST_TO_DEVICE)
        
        # 执行CANN路径规划内核
        self._execute_path_planning_kernel()
        
        # 获取输出路径
        path_data = np.zeros(self.max_path_length * 3, dtype=np.float32)
        acl.rt.memcpy(path_data.ctypes.data,
                     path_data.nbytes,
                     self.path_buffer,
                     path_data.nbytes,
                     acl.rt.memcpy_kind.DEVICE_TO_HOST)
        
        # 解析路径点
        path_points = path_data.reshape(-1, 3)
        
        # 去除无效点（零填充）
        valid_mask = np.any(path_points != 0, axis=1)
        path_points = path_points[valid_mask]
        
        return path_points
    
    def _generate_trajectory(self,
                           path: np.ndarray,
                           constraints: Optional[Dict]) -> Dict:
        """生成轨迹（速度、加速度剖面）"""
        
        if constraints is None:
            constraints = {
                'max_velocity': 1.0,    # 最大速度 m/s
                'max_acceleration': 0.5, # 最大加速度 m/s²
                'max_curvature': 0.3     # 最大曲率 1/m
            }
        
        # 计算路径长度
        path_length = self._compute_path_length(path)
        
        # 生成速度剖面
        velocity_profile = self._generate_velocity_profile(
            path, path_length, constraints
        )
        
        # 生成加速度剖面
        acceleration_profile = self._compute_acceleration_profile(
            velocity_profile
        )
        
        # 生成时间戳
        time_profile = self._compute_time_profile(velocity_profile, path)
        
        # 计算曲率
        curvature_profile = self._compute_curvature_profile(path)
        
        return {
            'path_points': path,
            'velocity_profile': velocity_profile,
            'acceleration_profile': acceleration_profile,
            'time_profile': time_profile,
            'curvature_profile': curvature_profile,
            'total_time': time_profile[-1] if len(time_profile) > 0 else 0.0,
            'total_distance': path_length
        }

class MultiAgentPlanner:
    """多智能体协同规划器"""
    
    def __init__(self, cann_accelerated=True):
        self.cann_accelerated = cann_accelerated
        
        # 冲突检测器
        self.collision_detector = CollisionDetector()
        
        # 协调策略
        self.coordination_strategies = {
            'priority_based': self._priority_based_coordination,
            'auction_based': self._auction_based_coordination,
            'game_theoretic': self._game_theoretic_coordination,
            'centralized': self._centralized_coordination
        }
        
        # 如果使用CANN加速
        if cann_accelerated:
            self.cann_coordinator = MultiAgentCANN()
        
        # 通信模拟器
        self.communicator = AgentCommunicator()
        
        print("[INFO] 多智能体协同规划器初始化完成")
    
    def coordinate_paths(self,
                        agent_plans: List[Dict],
                        environment_encoding: Dict,
                        strategy: str = 'priority_based') -> List[Dict]:
        """协调多个智能体的路径"""
        
        print(f"协调{len(agent_plans)}个智能体的路径，策略: {strategy}")
        
        # 1. 检测冲突
        conflicts = self.collision_detector.detect_conflicts(agent_plans)
        
        if not conflicts:
            print("没有检测到冲突")
            return agent_plans
        
        print(f"检测到{len(conflicts)}个冲突")
        
        # 2. 选择协调策略
        coordination_func = self.coordination_strategies.get(
            strategy, self._priority_based_coordination
        )
        
        # 3. 执行协调（CANN加速）
        if self.cann_accelerated and self.cann_coordinator is not None:
            coordinated_plans = self.cann_coordinator.coordinate(
                agent_plans, conflicts, strategy
            )
        else:
            coordinated_plans = coordination_func(
                agent_plans, conflicts, environment_encoding
            )
        
        # 4. 验证协调结果
        remaining_conflicts = self.collision_detector.detect_conflicts(
            coordinated_plans
        )
        
        if remaining_conflicts:
            print(f"协调后仍有{len(remaining_conflicts)}个冲突，尝试重新协调")
            # 递归协调或尝试不同策略
            return self._resolve_remaining_conflicts(
                coordinated_plans, remaining_conflicts, environment_encoding
            )
        
        print("所有冲突已解决")
        return coordinated_plans
    
    def _priority_based_coordination(self,
                                   agent_plans: List[Dict],
                                   conflicts: List[Dict],
                                   environment: Dict) -> List[Dict]:
        """基于优先级的协调策略"""
        
        # 分配优先级（可根据任务重要性、紧急程度等）
        priorities = self._assign_priorities(agent_plans)
        
        # 按优先级排序
        sorted_indices = np.argsort(priorities)[::-1]  # 降序
        
        # 高优先级智能体保持原路径，低优先级重新规划
        coordinated_plans = agent_plans.copy()
        
        for conflict in conflicts:
            agent1, agent2 = conflict['agents']
            
            # 比较优先级
            if priorities[agent1] >= priorities[agent2]:
                # agent1优先级高，agent2重新规划
                coordinated_plans[agent2] = self._replan_for_agent(
                    agent2, coordinated_plans, conflict, environment
                )
            else:
                # agent2优先级高，agent1重新规划
                coordinated_plans[agent1] = self._replan_for_agent(
                    agent1, coordinated_plans, conflict, environment
                )
        
        return coordinated_plans
    
    def _replan_for_agent(self,
                         agent_id: int,
                         current_plans: List[Dict],
                         conflict: Dict,
                         environment: Dict) -> Dict:
        """为指定智能体重新规划路径"""
        
        # 获取当前路径
        current_plan = current_plans[agent_id]
        
        # 获取冲突区域
        conflict_region = conflict['region']
        
        # 添加临时障碍物（其他智能体的路径段）
        temporary_obstacles = []
        for other_id, other_plan in enumerate(current_plans):
            if other_id != agent_id:
                # 添加其他智能体在冲突时间内的路径段
                obstacle_segment = self._extract_conflict_segment(
                    other_plan, conflict['time_window']
                )
                temporary_obstacles.append(obstacle_segment)
        
        # 更新环境编码（添加临时障碍物）
        updated_environment = self._add_temporary_obstacles(
            environment, temporary_obstacles
        )
        
        # 重新规划路径
        # 这里需要调用路径生成器，为简化展示，我们返回一个修改后的路径
        new_path = self._modify_path_to_avoid(
            current_plan['path'], conflict_region
        )
        
        # 更新计划
        new_plan = current_plan.copy()
        new_plan['path'] = new_path
        new_plan['replanned'] = True
        new_plan['conflict_avoided'] = conflict['id']
        
        return new_plan

class RealTimePathPlanner:
    """实时路径规划系统"""
    
    def __init__(self, config_path: str = "config/path_planner.json"):
        
        # 加载配置
        self.config = self._load_config(config_path)
        
        # 初始化核心组件
        self.environment_encoder = EnvironmentEncoderCANN(
            self.config['encoder_model']
        )
        
        self.path_generator = PathGeneratorCANN(
            model_path=self.config['generator_model'],
            device_id=self.config.get('device_id', 0)
        )
        
        self.multi_agent_planner = MultiAgentPlanner(
            cann_accelerated=self.config.get('cann_coordination', True)
        )
        
        # 本地化与建图
        self.localizer = LocalizationModule()
        self.mapper = MappingModule()
        
        # 预测模块
        self.predictor = TrajectoryPredictor()
        
        # 历史记录
        self.history = PlanningHistory(max_size=1000)
        
        # 性能监控
        self.metrics = {
            'total_plans': 0,
            'avg_planning_time': 0.0,
            'success_rate': 1.0,
            'replan_events': 0
        }
        
        print("[INFO] 实时路径规划系统初始化完成")
    
    def plan(self,
            start_point: np.ndarray,
            goal_point: np.ndarray,
            environment_data: Dict,
            agent_id: int = 0,
            constraints: Optional[Dict] = None) -> Dict:
        """执行单智能体路径规划"""
        
        start_time = time.time()
        self.metrics['total_plans'] += 1
        
        print(f"智能体 {agent_id} 路径规划请求")
        print(f"起点: {start_point}, 终点: {goal_point}")
        
        # 1. 环境编码
        environment_encoding = self.environment_encoder.encode_environment(
            environment_data, use_cann=True
        )
        
        # 2. 预测动态障碍物轨迹
        if 'dynamic_obstacles' in environment_encoding:
            predictions = self.predictor.predict_trajectories(
                environment_encoding['dynamic_obstacles']
            )
            environment_encoding['obstacle_predictions'] = predictions
        
        # 3. 路径规划
        plan_result = self.path_generator.plan_path(
            start_point=start_point,
            goal_point=goal_point,
            environment_encoding=environment_encoding,
            constraints=constraints
        )
        
        # 4. 记录历史
        self.history.record({
            'agent_id': agent_id,
            'start_point': start_point,
            'goal_point': goal_point,
            'plan_result': plan_result,
            'timestamp': time.time(),
            'environment_snapshot': environment_encoding
        })
        
        planning_time = time.time() - start_time
        
        # 更新性能指标
        old_avg = self.metrics['avg_planning_time']
        n = self.metrics['total_plans']
        self.metrics['avg_planning_time'] = (
            old_avg * (n-1) + planning_time
        ) / n
        
        return {
            'agent_id': agent_id,
            'plan_result': plan_result,
            'planning_time': planning_time,
            'environment_encoding': environment_encoding,
            'success': plan_result['feasible_path'] is not None
        }
    
    def multi_agent_plan(self,
                        agent_requests: List[Dict],
                        environment_data: Dict) -> Dict:
        """多智能体协同规划"""
        
        start_time = time.time()
        
        print(f"多智能体协同规划，{len(agent_requests)}个智能体")
        
        # 1. 环境编码（共享环境）
        environment_encoding = self.environment_encoder.encode_environment(
            environment_data, use_cann=True
        )
        
        # 2. 并行规划每个智能体的初始路径
        initial_plans = []
        
        for request in agent_requests:
            plan_result = self.plan(
                start_point=request['start'],
                goal_point=request['goal'],
                environment_data=environment_data,
                agent_id=request['id'],
                constraints=request.get('constraints')
            )
            initial_plans.append(plan_result)
        
        # 3. 多智能体协调
        coordinated_plans = self.multi_agent_planner.coordinate_paths(
            initial_plans, environment_encoding,
            strategy='priority_based'
        )
        
        # 4. 验证整体规划
        overall_valid = self._validate_overall_plan(coordinated_plans)
        
        total_time = time.time() - start_time
        
        return {
            'coordinated_plans': coordinated_plans,
            'overall_valid': overall_valid,
            'total_time': total_time,
            'agent_count': len(agent_requests),
            'initial_conflicts': len(initial_plans) - len(coordinated_plans)
        }
    
    def replan(self,
              current_plan: Dict,
              new_environment_data: Dict,
              reason: str = 'environment_change') -> Dict:
        """重新规划（响应环境变化）"""
        
        self.metrics['replan_events'] += 1
        
        print(f"重新规划，原因: {reason}")
        
        # 获取当前位置
        current_position = self.localizer.get_current_position()
        
        # 获取当前路径中未完成的部分
        remaining_path = self._extract_remaining_path(
            current_plan['path'], current_position
        )
        
        if len(remaining_path) == 0:
            print("没有剩余路径需要重新规划")
            return current_plan
        
        # 以当前位置为起点，原目标点为终点，重新规划
        new_plan = self.plan(
            start_point=current_position,
            goal_point=current_plan['goal'],
            environment_data=new_environment_data,
            agent_id=current_plan['agent_id'],
            constraints=current_plan.get('constraints')
        )
        
        # 标记为重新规划
        new_plan['replanned'] = True
        new_plan['replan_reason'] = reason
        new_plan['original_plan_id'] = current_plan.get('plan_id')
        
        return new_plan
    
    def _validate_overall_plan(self, plans: List[Dict]) -> bool:
        """验证整体规划的有效性"""
        
        # 检查每个智能体的规划是否有效
        for plan in plans:
            if not plan.get('success', False):
                print(f"智能体 {plan.get('agent_id')} 的规划失败")
                return False
        
        # 检查冲突
        # 这里可以调用冲突检测器
        
        return True

# 使用示例
if __name__ == "__main__":
    # 初始化路径规划系统
    planner = RealTimePathPlanner("config/planner_config.json")
    
    print("=== 实时路径规划系统测试 ===\n")
    
    # 测试用例1：单智能体规划
    print("测试1: 单智能体路径规划")
    
    environment_data = {
        'point_cloud': np.random.randn(1000, 3),  # 模拟点云
        'occupancy_grid': np.random.rand(100, 100, 10) > 0.1,  # 3D占据栅格
        'semantic_map': np.zeros((100, 100, 5)),  # 语义地图
        'sensor_data': {}  # 传感器数据
    }
    
    result1 = planner.plan(
        start_point=np.array([0, 0, 0]),
        goal_point=np.array([10, 10, 0]),
        environment_data=environment_data,
        agent_id=1,
        constraints={
            'max_velocity': 1.5,
            'max_acceleration': 0.8,
            'preferred_road_type': 'main_road'
        }
    )
    
    print(f"规划结果: {'成功' if result1['success'] else '失败'}")
    print(f"规划时间: {result1['planning_time']*1000:.2f}ms")
    print(f"路径长度: {result1['plan_result']['path_length']}个点")
    print(f"路径代价: {result1['plan_result']['path_cost']:.3f}")
    
    # 测试用例2：多智能体协同规划
    print("\n测试2: 多智能体协同规划")
    
    agent_requests = [
        {
            'id': 1,
            'start': np.array([0, 0, 0]),
            'goal': np.array([10, 10, 0]),
            'constraints': {'max_velocity': 1.0}
        },
        {
            'id': 2,
            'start': np.array([0, 10, 0]),
            'goal': np.array([10, 0, 0]),
            'constraints': {'max_velocity': 1.2}
        },
        {
            'id': 3,
            'start': np.array([5, 0, 0]),
            'goal': np.array([5, 10, 0]),
            'constraints': {'max_velocity': 0.8}
        }
    ]
    
    result2 = planner.multi_agent_plan(agent_requests, environment_data)
    
    print(f"协同规划结果: {'有效' if result2['overall_valid'] else '无效'}")
    print(f"总时间: {result2['total_time']:.2f}秒")
    print(f"智能体数量: {result2['agent_count']}")
    print(f"初始冲突数: {result2['initial_conflicts']}")
    
    # 性能报告
    metrics = planner.get_performance_metrics()
    print("\n=== 性能报告 ===")
    for key, value in metrics.items():
        if isinstance(value, float):
            print(f"{key}: {value:.3f}")
        else:
            print(f"{key}: {value}")

class PathPlanningOptimizer:
    """路径规划的CANN优化器"""
    
    @staticmethod
    def optimize_for_realtime():
        """实时性优化配置"""
        return {
            "graph_processing": {
                "parallel_node_expansion": True,  # 并行节点扩展
                "priority_queue_acceleration": True,  # 优先队列加速
                "incremental_search": True  # 增量搜索
            },
            "memory_optimization": {
                "cost_map_compression": True,  # 代价地图压缩
                "path_cache": True,  # 路径缓存
                "incremental_updates": True  # 增量更新
            },
            "computation_optimization": {
                "adaptive_resolution": True,  # 自适应分辨率
                "heuristic_acceleration": True,  # 启发式加速
                "batch_planning": True  # 批量规划
            }
        }