diff --git a/README.md b/README.md index 974ff5e..42c9238 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,25 @@ -# 这是一个仅供***个人瞎搞***的东西 +# 这是一个***小白瞎搞***的东西,仅供参考(我甚至觉得不能作为参考) 这些会部署在香橙派AI Pro上，模型为*YOLOv26n* -### 说明下各文件的作用 +## 说明下各文件的作用 +#### *python部分* +#### **零散脚本** - check.py:确认输入视频帧率，用于性能测试的; - control.py:PID计算和MAVLink指令发送方法的定义(已废弃); - fc_test:获取飞控信息的测试脚本; - infer_camera_direct.py:通过摄像头检测目标并调用方法控制无人机; -- infer_camera_modular.py:模块化处理过的摄像头检测与控制(未测试) -- infer_camera_video.py:使用视频检测目标并调用方法控制无人机(用于测试); - infer.py:使用图片检测目标，仅用于测试环境; -#### 无人机控制部分: -- base_control.py:定义了部分基本动作的方法 -#### 视觉跟踪业务: -- pid_counter.py:抽离出来的PID计算器 -#### 自动巡点飞行业务: -- mission_manager.py:自动巡点飞行的控制模块 +- infer_camera_modular.py:模块化处理过的摄像头检测与控制(未测试) +- infer_camera_video.py:使用视频检测目标并调用方法控制无人机(用于测试,未调参) +- gent_mission.py:尝试将视觉追踪与自动巡点飞行相结合(未测试，这应该是本项目一阶段的最终预期) - run_mission.py:自动巡点飞行的业务测试(未测试) -#### 目录： +#### drone_controller:控制模块 +- base_control.py:定义了部分基本动作的方法 +- pid_counter.py:抽离出来的PID计算器 +- mission_manager.py:自动巡点飞行的控制模块 +#### 其他目录以及文件： - om:推理使用的.om文件 -- drone_controller:控制模块 \ No newline at end of file +- asset:用于基础测试的文件 +- config.json:配置文件 +- start_npu_infer:一个.sh文件,计划后续将整个脚本作为一个服务 \ No newline at end of file diff --git a/drone_controller/mission_manager.py b/drone_controller/mission_manager.py index 8b74764..f80b167 100644 --- a/drone_controller/mission_manager.py +++ b/drone_controller/mission_manager.py @@ -8,149 +8,176 @@ class MissionState(Enum): ARMING = 1 # 解锁中 TAKEOFF = 2 # 自动起飞中 NAVIGATING = 3 # 航点巡航导航 - HOLD_TASK = 4 # 到达航点后的原地任务悬停 - RETURNING = 5 # 返航或直接准备降落 - LANDING = 6 # 自动降落中 - FINISHED = 7 # 任务结束/已上锁 - HOLD_FINAL = 8 # 结束巡点后原地悬停（当 return_to_home 为 False 时使用） + HOLD_TASK = 4 # 到达航点后的原地搜索/悬停 + VISUAL_TRACKING = 5 # 🎯 视觉追踪中（由视觉主循环接管控制权） + RETURNING = 6 # 返航中 + LANDING = 7 # 自动降落中 + FINISHED = 8 # 任务结束/已上锁 + HOLD_FINAL = 9 # 结束巡点后原地悬停（当 return_to_home 为 False 时使用） + class FailsafeTriggered(Exception): """自定义安全异常：当安全员人工介入切出自控模式时抛出，用于暴力熔断主循环""" pass + class MissionManager: def __init__(self, drone, waypoints, target_altitude=1.5, arrival_radius=0.3, hold_duration=5.0, return_to_home=True): """ :param drone: 实例化的 DroneController 底层驱动对象 - :param waypoints: 航点列表，格式为 [{'x': 1.0, 'y': 2.0, 'z': -1.5, 'yaw': 0}, ... ] - 注意：NED 坐标系中，Z轴向上为负，所以起飞或目标高度应为负值 - :param target_altitude: 初始起飞高度（米，正数，内部会自动转为NED负值） - :param arrival_radius: 判定航点到达的三维欧氏距离阈值（米） - :param hold_duration: 在每个航点到达后，执行任务（或悬停）的停留时间（秒） - :param return_to_home: 巡点结束后是否返回起始点降落。如果为 False，则在最后一个航点原地悬停。 + :param waypoints: 航点列表 [{'x': 1.0, 'y': 2.0, 'z': -1.5, 'yaw': 0}, ... ] + :param target_altitude: 默认起飞高度（米，NED中向上为负值） + :param arrival_radius: 航点到达判定半径（米） + :param hold_duration: 抵达巡点后，没有发现目标时的最大搜寻/悬停时间（秒） + :param return_to_home: 航点巡完后是否返航，False则在最后一个点原地悬停 """ self.drone = drone self.waypoints = waypoints - self.target_altitude = -abs(target_altitude) # 强制转为 NED 负值 + self.target_altitude = target_altitude if target_altitude < 0 else -target_altitude self.arrival_radius = arrival_radius self.hold_duration = hold_duration self.return_to_home = return_to_home self.state = MissionState.INIT self.current_wp_index = 0 - self.hold_start_time = 0.0 - # 记录起始点坐标，用于返航 + # 计时器定义 + self.hold_start_time = 0.0 + self.tracking_start_time = 0.0 + + # 起始点（Home）坐标缓存 self.home_x = 0.0 self.home_y = 0.0 - self.home_z = self.target_altitude + self.home_z = 0.0 - def get_distance(self, target_wp): - """计算当前无人机局部 NED 坐标到目标航点的三维欧氏距离""" - curr_x, curr_y, curr_z = self.drone.local_position - dx = target_wp['x'] - curr_x - dy = target_wp['y'] - curr_y - dz = target_wp['z'] - curr_z - return math.sqrt(dx ** 2 + dy ** 2 + dz ** 2) - - def update(self): + def _is_tracking_task_complete(self): """ - 状态机核心主循环，需由上位机主程序以 5Hz~10Hz 的高频流式调用。 - 包含链路维持、安全熔断以及新增的原地悬停/返航分支。 + 🛠️ 预留接口：判断视觉追踪任务是否完成 + 你可以根据需求修改内部逻辑。例如： + - 当前：假设持续追踪 5 秒视为完成任务。 + - 后续扩展：可以引入与目标的物理距离、传感器状态等。 """ - # 1. 专属安全熔断机制：一旦检测到场外安全员切回手动模式，立即熔断 - if self.drone.current_mode not in ['GUIDED', 'OFFBOARD'] and self.state != MissionState.INIT: - print(f"\n⚠️ [安全熔断] 检测到飞控退出自动控制模式(当前模式: {self.drone.current_mode})！上位机状态机退出。") - self.state = MissionState.FINISHED - return False + tracking_duration = time.time() - self.tracking_start_time + target_hold_time = 5.0 # 默认设定5秒 - # 2. 状态机逻辑分支 + if tracking_duration >= target_hold_time: + return True + return False + + def update(self, target_detected=False): + """ + 状态机高频核心业务逻辑，由外部主循环驱动 + :param target_detected: 当前帧是否发现了视觉追踪目标 + """ + # 0. 越权检查（最强人肉兜底）：只要切出自主控制模式，立刻暴力熔断 + if self.state not in [MissionState.INIT, MissionState.FINISHED]: + if self.drone.current_mode not in ['GUIDED', 'OFFBOARD']: + print(f"\n⚠️ [Failsafe] 检测到飞控模式被切为: {self.drone.current_mode}! 外部人工强行接管，熔断任务！") + self.state = MissionState.FINISHED + raise FailsafeTriggered("安全员强行接管") + + # 1. INIT: 等待飞控连接并获取当前位置作为 Home 点 if self.state == MissionState.INIT: if self.drone.is_connected: - print("\n[通信成功] 无人机已连接，准备记录起始点并解锁...") - # 记录解锁时的局部位置作为 Home 点 - hx, hy, _ = self.drone.local_position - self.home_x = hx - self.home_y = hy - self.state = MissionState.ARMING - else: - print("\r[等待中] 等待飞控心跳连接...", end="", flush=True) + print("[INIT] 飞控已连上。获取当前坐标为起始点...") + self.home_x, self.home_y, self.home_z = self.drone.local_position + print(f"[INIT] 起始点坐标: ({self.home_x:.2f}, {self.home_y:.2f}, {self.home_z:.2f})") + print("[INIT] 正在下发 GUIDED/OFFBOARD 模式并尝试解锁...") - elif self.state == MissionState.ARMING: - print("\r[指令下发] 正在尝试解锁电机...", end="", flush=True) - if self.drone.is_armed: - print("\n[成功] 电机已解锁！准备切入控制模式并起飞。") - # 引导无人机切入 GUIDED 或 OFFBOARD - target_mode = 'OFFBOARD' if 'PX4' in getattr(self.drone, 'firmware_type', 'PX4') else 'GUIDED' + target_mode = 'GUIDED' if 'guided' in self.drone.current_mode.lower() or True else 'OFFBOARD' self.drone.set_mode(target_mode) - self.state = MissionState.TAKEOFF - else: self.drone.arm() - time.sleep(0.5) + self.state = MissionState.ARMING + # 2. ARMING: 监测解锁状态 + elif self.state == MissionState.ARMING: + if self.drone.is_armed and self.drone.current_mode in ['GUIDED', 'OFFBOARD']: + print("\n[解锁成功] 正在下发自动起飞指令...") + # 下发位置到期望高度 + self.drone.send_target_position(self.home_x, self.home_y, self.target_altitude, 0) + self.state = MissionState.TAKEOFF + + # 3. TAKEOFF: 自动起飞爬升判定 elif self.state == MissionState.TAKEOFF: - # 虚拟/流式起飞：高频下发起飞期望高度，维持链路活性 + current_z = self.drone.local_position[2] + # 持续发送起飞点位置，激活控制链路 self.drone.send_target_position(self.home_x, self.home_y, self.target_altitude, 0) - # 判定是否到达起飞高度 - _, _, curr_z = self.drone.local_position - if abs(curr_z - self.target_altitude) < self.arrival_radius: - print(f"\n[完成] 已到达安全起飞高度: {abs(curr_z):.2f}m。开始巡航任务...") - if len(self.waypoints) > 0: - self.current_wp_index = 0 - self.state = MissionState.NAVIGATING - else: - print("⚠️ 警告: 航点列表为空！") - if self.return_to_home: - self.state = MissionState.RETURNING - else: - self.state = MissionState.HOLD_FINAL - else: - print(f"\r[起飞中] 当前高度: {abs(curr_z):.2f}m / 目标: {abs(self.target_altitude):.2f}m", end="", - flush=True) + # NED 坐标系下 Z 为负数，判断是否接近目标高度 + if abs(current_z - self.target_altitude) < self.arrival_radius: + print("\n[起飞完成] 达到预定高度。开始前往第 1 个巡航航点...") + self.state = MissionState.NAVIGATING + self.current_wp_index = 0 + # 4. NAVIGATING: 航点导航巡航 elif self.state == MissionState.NAVIGATING: + if not self.waypoints or self.current_wp_index >= len(self.waypoints): + # 航点全部飞完 + if self.return_to_home: + print("\n[巡航完成] 所有航点已巡视完毕。正在返回起始点上方...") + self.state = MissionState.RETURNING + else: + print("\n[巡航完成] 所有航点已巡视完毕。根据配置切换至末端就地悬停...") + self.state = MissionState.HOLD_FINAL + return + + # 获取当前目标航点 wp = self.waypoints[self.current_wp_index] - # “零期望命令”防失控刹车：流式高频持续推流当前目标点 self.drone.send_target_position(wp['x'], wp['y'], wp['z'], wp['yaw']) - dist = self.get_distance(wp) - print(f"\r[任务中] 正在飞往航点 [{self.current_wp_index + 1}] | 剩余距离: {dist:.2f}m", end="", flush=True) + # 计算水平及高度距离 + dx = wp['x'] - self.drone.local_position[0] + dy = wp['y'] - self.drone.local_position[1] + dz = wp['z'] - self.drone.local_position[2] + dist_xyz = math.sqrt(dx ** 2 + dy ** 2 + dz ** 2) - if dist < self.arrival_radius: - print(f"\n[到达] 已精准触达航点 [{self.current_wp_index + 1}]，开启原地任务阶段。") - self.hold_start_time = time.time() - self.state = MissionState.HOLD_TASK - - elif self.state == MissionState.HOLD_TASK: - wp = self.waypoints[self.current_wp_index] - # 悬停期间同样高频重发当前坐标，让飞控稳定在原地 - self.drone.send_target_position(wp['x'], wp['y'], wp['z'], wp['yaw']) - - elapsed = time.time() - self.hold_start_time print( - f"\r[任务中] 航点 [{self.current_wp_index + 1}] 原地悬停检测中... 已耗时: {elapsed:.1f}s / {self.hold_duration}s", + f"\n\r[巡航中] 前往航点 [{self.current_wp_index + 1}/{len(self.waypoints)}] | 距离目标: {dist_xyz:.2f}m", end="", flush=True) - if elapsed >= self.hold_duration: - print(f"\n[完成] 航点 [{self.current_wp_index + 1}] 任务执行完毕。") - # 切换下一个点 - self.current_wp_index += 1 - if self.current_wp_index < len(self.waypoints): - self.state = MissionState.NAVIGATING - else: - # 【核心修改点】所有航点巡视完毕，根据参数决定去向 - if self.return_to_home: - print("\n[完成] 所有航点均已巡视完毕！准备返航起始点。") - self.state = MissionState.RETURNING - else: - print("\n[完成] 所有航点均已巡视完毕！[设置] 不执行返航，就地保持悬停。") - self.state = MissionState.HOLD_FINAL + if dist_xyz < self.arrival_radius: + print(f"\n[到达航点 {self.current_wp_index + 1}] 进入原地悬停与视觉检索阶段...") + self.state = MissionState.HOLD_TASK + self.hold_start_time = time.time() + # 5. HOLD_TASK: 抵达巡点后的搜索悬停阶段 + elif self.state == MissionState.HOLD_TASK: + # 优先判定视觉：如果在巡点悬停搜寻期间，视觉发现了目标 + if target_detected: + print("\n🎯 [目标锁定] 视觉检测到目标！立刻中断悬停，切入 [视觉追踪] 状态！") + self.state = MissionState.VISUAL_TRACKING + self.tracking_start_time = time.time() + return + + # 如果没有发现目标，则保持航点位置，并检查悬停是否超时 + wp = self.waypoints[self.current_wp_index] + self.drone.send_target_position(wp['x'], wp['y'], wp['z'], wp['yaw']) + + elapsed_time = time.time() - self.hold_start_time + print(f"\r[检索中] 保持航点中... 剩余搜寻时间: {max(0.0, self.hold_duration - elapsed_time):.1f}s", end="", + flush=True) + + if elapsed_time >= self.hold_duration: + print(f"\n[未发现目标] 搜寻超时。放弃当前点，前往下一个航点...") + self.current_wp_index += 1 + self.state = MissionState.NAVIGATING + + # 6. VISUAL_TRACKING: 🎯 视觉闭环追踪状态（核心新增） + elif self.state == MissionState.VISUAL_TRACKING: + # 在此状态下，高层状态机【不主动发送】绝对位置坐标，把控制权完全让渡给主循环的 PID 速度控制 + # 我们只需要在这里进行任务结束判定 + print(f"\r[追踪中] 正在执行AI视觉闭环精准追踪... 计时: {time.time() - self.tracking_start_time:.1f}s", + end="", flush=True) + + if self._is_tracking_task_complete(): + print("\n✅ [任务完成] 当前目标追踪任务圆满完成！重置追踪器，继续执行航点巡航...") + self.current_wp_index += 1 # 切换到下一个航点 + self.state = MissionState.NAVIGATING + + # 7. RETURNING: 自动返航至起始点上方 elif self.state == MissionState.RETURNING: - # 飞回记录的 Home 起始点高度位置 - self.drone.send_target_position(self.home_x, self.home_y, self.home_z, 0) + self.drone.send_target_position(self.home_x, self.home_y, self.target_altitude, 0) dist_to_home = math.sqrt((self.home_x - self.drone.local_position[0]) ** 2 + (self.home_y - self.drone.local_position[1]) ** 2) @@ -163,25 +190,19 @@ class MissionManager: else: print("⚠️ 降落模式切换失败，正在重新下发...") + # 8. LANDING: 自动降落中 + elif self.state == MissionState.LANDING: + print(f"\r[降落中] 当前高度: {self.drone.local_position[2]:+.2f}m | 解锁状态: {self.drone.is_armed}", + end="", flush=True) + if not self.drone.is_armed: + print("\n[完结] 无人机已安全着陆并自动上锁。整个任务流结束。") + self.state = MissionState.FINISHED + + # 9. HOLD_FINAL: 末端就地悬停 elif self.state == MissionState.HOLD_FINAL: - # 【新增状态】不返航时的原地最终悬停状态 - # 锁定在最后一个航点，持续高频推流，维持控制链路，防止 Failsafe 刹车 final_wp = self.waypoints[-1] if self.waypoints else {'x': self.home_x, 'y': self.home_y, 'z': self.target_altitude, 'yaw': 0} self.drone.send_target_position(final_wp['x'], final_wp['y'], final_wp['z'], final_wp['yaw']) print( f"\r[挂起] 无人机保持在最终航点悬停。坐标:({final_wp['x']}, {final_wp['y']}, {final_wp['z']}) | 等待人工接管中...", - end="", flush=True) - - elif self.state == MissionState.LANDING: - # 判断是否已经安全着陆并自动上锁 - if not self.drone.is_armed: - print("\n[完成] 无人机已安全着陆并自动上锁，任务圆满结束！") - self.state = MissionState.FINISHED - else: - print("\r[降落中] 正在下降着陆中...", end="", flush=True) - - elif self.state == MissionState.FINISHED: - return False - - return True \ No newline at end of file + end="", flush=True) \ No newline at end of file diff --git a/gent_mission.py b/gent_mission.py new file mode 100644 index 0000000..8e20efb --- /dev/null +++ b/gent_mission.py @@ -0,0 +1,296 @@ +import os +import cv2 +import numpy as np +import time +import threading +import queue +import socket +import json +import sys +from ais_bench.infer.interface import InferSession + +# ================== 导入基础控制与状态机模块 ===================== +from drone_controller.base_control import DroneController +from drone_controller.pid_counter import PID +from drone_controller.mission_manager import MissionManager, MissionState, FailsafeTriggered +# =================================================================== + +class VideoStreaming: + """ 📡 视频流采集与后台图传模块 """ + + def __init__(self, video_cfg, net_cfg): + self.video_source = video_cfg.get("video_source", 0) + self.jpeg_quality = video_cfg.get("jpeg_quality", 75) + self.gs_ip = net_cfg.get("ground_station_ip", "127.0.0.1") + self.udp_port = net_cfg.get("udp_port", 9999) + + self.cap = cv2.VideoCapture(self.video_source) + self.tx_queue = queue.Queue(maxsize=2) + self.is_running = True + + if not self.cap.isOpened(): + raise RuntimeError(f"❌ 无法打开视频源: {self.video_source}") + + self.sender_thread = threading.Thread(target=self._udp_stream_sender_worker, daemon=True) + self.sender_thread.start() + + def read_frame(self): + return self.cap.read() + + def push_frame(self, frame): + if not self.tx_queue.full(): + self.tx_queue.put(frame.copy()) + + def _udp_stream_sender_worker(self): + udp_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + dest_addr = (self.gs_ip, self.udp_port) + print(f"📡 [图传后台] UDP 推流服务已启动，目标地面站: {self.gs_ip}:{self.udp_port}") + + while self.is_running: + try: + frame = self.tx_queue.get(timeout=1.0) + ret, jpeg_buf = cv2.imencode('.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), self.jpeg_quality]) + if not ret: + continue + + data = jpeg_buf.tobytes() + packet_size = 60000 + total_size = len(data) + + for i in range(0, total_size, packet_size): + udp_socket.sendto(data[i:i + packet_size], dest_addr) + except queue.Empty: + continue + except Exception as e: + print(f"⚠️ [图传异常] 发送失败: {e}") + udp_socket.close() + + def release(self): + self.is_running = False + if self.cap.isOpened(): + self.cap.release() + + +class YOLO26UAVInfer: + """ 🧠 昇腾 NPU YOLOv8 极致推理模块 """ + + def __init__(self, model_cfg): + self.model_path = model_cfg.get("model_path", "./om/yolo26n-balloon.om") + self.conf_threshold = model_cfg.get("conf_threshold", 0.25) + device_id = model_cfg.get("device_id", 0) + + print(f"🧠 [NPU 初始化] 正在载入昇腾 OM 模型: {self.model_path} ...") + self.session = InferSession(device_id, self.model_path) + + def preprocess(self, img, img_size=640): + h, w = img.shape[:2] + r = min(img_size / h, img_size / w) + unpad_h, unpad_w = int(round(h * r)), int(round(w * r)) + + dw, dh = img_size - unpad_w, img_size - unpad_h + dw /= 2 + dh /= 2 + + if (w, h) != (unpad_w, unpad_h): + img_resized = cv2.resize(img, (unpad_w, unpad_h), interpolation=cv2.INTER_LINEAR) + else: + img_resized = img + + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img_padded = cv2.copyMakeBorder(img_resized, top, bottom, left, right, cv2.BORDER_CONSTANT, + value=(114, 114, 114)) + + img_in = img_padded.transpose((2, 0, 1))[::-1] # BGR to RGB + img_in = np.ascontiguousarray(img_in).astype(np.float32) / 255.0 + return img_in, r, (dw, dh) + + def postprocess(self, outputs, ratio, dwdh, orig_shape): + boxout = outputs[0][0] + box_num = outputs[1][0] + + detections = [] + for i in range(box_num): + box = boxout[i] + score = box[4] + cls_id = int(box[5]) + + if score < self.conf_threshold: + continue + + x1, y1, x2, y2 = box[0], box[1], box[2], box[3] + x1 -= dwdh[0] + x2 -= dwdh[0] + y1 -= dwdh[1] + y2 -= dwdh[1] + + x1, x2 = x1 / ratio, x2 / ratio + y1, y2 = y1 / ratio, y2 / ratio + + x1 = max(0, min(x1, orig_shape[1])) + y1 = max(0, min(y1, orig_shape[0])) + x2 = max(0, min(x2, orig_shape[1])) + y2 = max(0, min(y2, orig_shape[0])) + + detections.append({ + "box": [int(x1), int(y1), int(x2), int(y2)], + "score": float(score), + "class": cls_id + }) + return detections + + def draw_boxes(self, frame, detections): + for det in detections: + x1, y1, x2, y2 = det["box"] + cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) + cv2.putText(frame, f"Target: {det['score']:.2f}", (x1, y1 - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) + return frame + + +class UAVControlLoop: + """ ✈️ 融合型无人机控制闭环中心（深度适配状态机） """ + + def __init__(self, flight_cfg, pid_y_cfg, pid_z_cfg): + self.uav = DroneController( + connection_string=flight_cfg.get("connection_string", "/dev/ttyUSB0"), + baud=flight_cfg.get("baud_rate", 57600) + ) + self.pid_y = PID(**pid_y_cfg) + self.pid_z = PID(**pid_z_cfg) + + def start_uav(self): + print("⚡ [底层通信] 正在建立与飞控的 MAVLink 链路...") + if self.uav.connect(): + print("✅ [底层通信] 飞控监听守护线程就绪。") + return True + print("❌ [底层通信] 串口独占失败或未连上。") + return False + + def process_control(self, detections, orig_shape, frame, mission_state): + """ + 根据当前状态机状态，决定是否发送闭环 PID 控制速度 + """ + h, w = orig_shape + center_x_target, center_y_target = w / 2.0, h / 2.0 + + # 绘制画面正中央的十字瞄准准心 + cv2.drawMarker(frame, (int(center_x_target), int(center_y_target)), (0, 0, 255), cv2.MARKER_CROSS, 20, 2) + + # 判定是否包含有效检测目标 + if len(detections) > 0: + best_det = max(detections, key=lambda x: x["score"]) + x1, y1, x2, y2 = best_det["box"] + + # 计算目标的像素中心点 + obj_x = (x1 + x2) / 2.0 + obj_y = (y1 + y2) / 2.0 + + # 绘制目标质点追踪轨迹 + cv2.circle(frame, (int(obj_x), int(obj_y)), 5, (255, 0, 0), -1) + cv2.line(frame, (int(center_x_target), int(center_y_target)), (int(obj_x), int(obj_y)), (255, 255, 0), 2) + + # 归一化百分比误差计算值 + err_x = (obj_x - center_x_target) / center_x_target + err_y = (obj_y - center_y_target) / center_y_target + + # 核心控制逻辑决策：只有当状态机真正进入 VISUAL_TRACKING 状态，香橙派才拥有物理速度控制权！ + if mission_state == MissionState.VISUAL_TRACKING: + vy_cmd = self.pid_y.update(err_x) + vz_cmd = self.pid_z.update(err_y) + + # 下发机体系速度：前房保持0（悬停对准），vy左右平移，vz上下爬升 + self.uav.send_body_velocity(vx=0.0, vy=vy_cmd, vz=vz_cmd, yaw_rate=0.0) + else: + # 目标丢失兜底保护：如果在追踪状态下目标跟丢了，立即刹车原地悬停，并清除积分项 + if mission_state == MissionState.VISUAL_TRACKING: + self.uav.send_body_velocity(0, 0, 0, 0) + self.pid_y.reset() + self.pid_z.reset() + + +def load_config(config_path="./config.json"): + with open(config_path, "r", encoding="utf-8") as f: + return json.load(f) + + +if __name__ == "__main__": + # 模拟外部定义的自动化航点列表（NED坐标系：北, 东, 地。Z 轴向上为负，所以高度 1.5 米设为 -1.5） + test_waypoints = [ + {'x': 1.0, 'y': 0.0, 'z': -1.5, 'yaw': 0}, + {'x': 2.0, 'y': 1.5, 'z': -1.5, 'yaw': 45}, + {'x': 0.0, 'y': 2.0, 'z': -1.5, 'yaw': 90} + ] + + cfg = load_config() + + # 实例化子系统 + streamer = VideoStreaming(cfg["video"], cfg["network"]) + detector = YOLO26UAVInfer(cfg["model"]) + controller = UAVControlLoop(cfg["flight_control"], cfg["pid_y"], cfg["pid_z"]) + + # 启动无人机串口连接 + if not controller.start_uav(): + sys.exit("❌ 飞控连接失败，无法继续业务") + + # 实例化复合状态机业务管理器，配置不发现目标时巡点悬停检索时间为 8 秒，飞完直接原地降落不返航 + mission = MissionManager( + drone=controller.uav, + waypoints=test_waypoints, + target_altitude=1.5, + arrival_radius=0.3, + hold_duration=8.0, + return_to_home=False + ) + + print("🚀 NPU 精准推理 [航点巡航与视觉追踪融合版] 主业务循环全面启动...") + + try: + while True: + ret, frame = streamer.read_frame() + if not ret: + print("❌ 未能读取到摄像头数据，退出中...") + break + + orig_shape = frame.shape[:2] + + # 1. 图像预处理与硬件 NPU 高速推理 + input_tensor, ratio, dwdh = detector.preprocess(frame) + outputs = detector.session.infer([input_tensor]) + + # 2. 坐标反求与后处理 + detections = detector.postprocess(outputs, ratio, dwdh, orig_shape) + + # 判断当前帧是否捕捉到了目标 + target_detected = len(detections) > 0 + + # 3. 驱动高层任务管理器状态机（传入视觉反馈） + try: + mission.update(target_detected=target_detected) + except FailsafeTriggered: + # 触发人肉拦截，跳出死循环，优雅关闭程序 + break + + # 4. 驱动底层视觉闭环速度计算（只有状态机进入 VISUAL_TRACKING 时才会真正发送速度指令） + controller.process_control(detections, orig_shape, frame, mission.state) + + # 5. 地面站视频帧渲染绘制 + frame = detector.draw_boxes(frame, detections) + + # 在图传画面上实时叠加当前系统状态机状态 + status_text = f"State: {mission.state.name} | WP: {mission.current_wp_index + 1}/{len(test_waypoints)}" + cv2.putText(frame, status_text, (20, 40), cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 2) + + # 推送视频到后台队列传输至 PC + streamer.push_frame(frame) + + # 限制主循环高频速度（30Hz 左右），防止高频过度占用 CPU + time.sleep(0.033) + + except KeyboardInterrupt: + print("\n👋 接收到终端键盘中断指令，正在安全退出...") + finally: + # 优雅释放 + streamer.release() + controller.uav.close() + print("🏁 系统资源已安全回收，程序退出。") \ No newline at end of file