wenchunandClaude Opus 4.7
415aed088a
1. mavlinkROS2Nodes: position_gnss topic 改發 relative_altitude 而非 AMSL， 讓 drone_gps['alt'] 在實飛與 SITL 都代表「相對 home 高度」 2. mission_planner: Circle stage1.z 改用 final_z（不再用 current_z 做中點插值）， 避免實飛時 current_z 是 AMSL 而把過渡高度衝到海拔值的一半 3. mission_executor: 到達判定改為 hover-stable（radius * 1.5 hysteresis + 穩定計時），預設 arrival_radius 2→4m，解決實飛 GPS 抖動永遠進不了 2m radius 的卡死問題 4. mission_executor: 新增 progress log，每 3 秒印一行距離與穩定度， 方便實飛 debug 卡點問題 5. mission_group + gui: 暴露 arrival_radius / hover_stable_sec 給 GUI 任務參數面板， 每次 start 重讀以支援中途調整 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
550 lines
22 KiB
Python
550 lines
22 KiB
Python
#!/usr/bin/env python3
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"""
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飛行任務規劃模組
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支援: M-Formation, Circle, Leader-Follower (arc-length virtual leader), Grid Sweep
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"""
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import math
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from typing import List, Tuple, Optional, Dict, Any
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from enum import Enum
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class MissionType(Enum):
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"""任務類型"""
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M_FORMATION = "m_formation"
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CIRCLE_FORMATION = "circle_formation"
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LEADER_FOLLOWER = "leader_follower"
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GRID_SWEEP = "grid_sweep"
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class CoordinateConverter:
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"""GPS 座標與 Local 座標的轉換器"""
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def __init__(self, origin_lat: float, origin_lon: float, origin_alt: float = 0):
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self.origin_lat = origin_lat
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self.origin_lon = origin_lon
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self.origin_alt = origin_alt
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self.R = 6371000.0
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def gps_to_local(self, lat: float, lon: float, alt: float) -> Tuple[float, float, float]:
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lat_rad = math.radians(lat)
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lon_rad = math.radians(lon)
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origin_lat_rad = math.radians(self.origin_lat)
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origin_lon_rad = math.radians(self.origin_lon)
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dlat = lat_rad - origin_lat_rad
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dlon = lon_rad - origin_lon_rad
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x = self.R * dlon * math.cos((lat_rad + origin_lat_rad) / 2)
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y = self.R * dlat
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z = alt - self.origin_alt
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return x, y, z
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def local_to_gps(self, x: float, y: float, z: float) -> Tuple[float, float, float]:
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origin_lat_rad = math.radians(self.origin_lat)
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origin_lon_rad = math.radians(self.origin_lon)
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lat_rad = origin_lat_rad + (y / self.R)
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lon_rad = origin_lon_rad + (x / (self.R * math.cos((lat_rad + origin_lat_rad) / 2)))
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lat = math.degrees(lat_rad)
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lon = math.degrees(lon_rad)
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alt = self.origin_alt + z
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return lat, lon, alt
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class FormationPlanner:
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"""隊形規劃器"""
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def __init__(self, spacing: float = 5.0,
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base_altitude: float = 10.0,
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altitude_diff: float = 2.0):
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self.spacing = spacing
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self.base_altitude = base_altitude
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self.altitude_diff = altitude_diff
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self.current_origin = None
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self.converter = None
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def plan_formation_mission(self,
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drone_gps_positions: List[Tuple[float, float, float]],
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target_gps: Tuple[float, float, float],
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mission_type: MissionType = MissionType.M_FORMATION,
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params: Optional[Dict[str, Any]] = None
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) -> Tuple[List[List[Tuple[float, float, float]]],
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Tuple[float, float, float],
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List[int]]:
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"""
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回傳 (waypoints_gps, origin, rendezvous_indices)。
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rendezvous_indices：航點序列裡該群組需要等全員到齊才推進的 index 清單。
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"""
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if len(drone_gps_positions) == 0:
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raise ValueError("無人機位置列表不能為空")
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center_lat = sum(pos[0] for pos in drone_gps_positions) / len(drone_gps_positions)
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center_lon = sum(pos[1] for pos in drone_gps_positions) / len(drone_gps_positions)
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# origin_alt = 0：送給飛控的高度透過 MAV_FRAME_GLOBAL_RELATIVE_ALT_INT
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# 直接就是「相對 home 高度」，不需要加 GPS 橢球高度偏移
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self.current_origin = (center_lat, center_lon, 0.0)
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self.converter = CoordinateConverter(center_lat, center_lon, 0.0)
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drone_local = [self.converter.gps_to_local(*pos) for pos in drone_gps_positions]
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target_local = self.converter.gps_to_local(*target_gps)
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if mission_type == MissionType.M_FORMATION:
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s1, s2 = self._calculate_m_formation(drone_local, target_local, params)
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waypoints_local = [[s1[i], s2[i]] for i in range(len(drone_local))]
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# 起點集合後一起出發到 stage2
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rendezvous_indices = [0]
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elif mission_type == MissionType.CIRCLE_FORMATION:
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s1, s2 = self._calculate_circle_formation(drone_local, target_local, params)
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waypoints_local = [[s1[i], s2[i]] for i in range(len(drone_local))]
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# 半程集合後一起進最終圓環位置
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rendezvous_indices = [0]
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elif mission_type == MissionType.LEADER_FOLLOWER:
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params = params or {}
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route_wps_gps = params.get('route_waypoints')
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if route_wps_gps is None or len(route_wps_gps) < 2:
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raise ValueError("LEADER_FOLLOWER 需要至少 2 個路徑點")
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route_wps_local = [
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self.converter.gps_to_local(wp[0], wp[1], 0)[:2]
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for wp in route_wps_gps
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]
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waypoints_local, rendezvous_indices = self._calculate_leader_follower(
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drone_local, route_wps_local, params
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)
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elif mission_type == MissionType.GRID_SWEEP:
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params = params or {}
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rect_corners_gps = params.get('rect_corners')
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if rect_corners_gps is None or len(rect_corners_gps) != 4:
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raise ValueError("GRID_SWEEP 需要 4 個 GPS 角點")
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rect_corners_local = [
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self.converter.gps_to_local(c[0], c[1], 0)[:2]
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for c in rect_corners_gps
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]
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waypoints_local, rendezvous_indices = self._calculate_grid_sweep(
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drone_local, rect_corners_local, params
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)
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else:
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raise ValueError(f"不支援的任務類型: {mission_type}")
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waypoints_gps = []
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for drone_wps in waypoints_local:
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gps_wps = [self.converter.local_to_gps(*wp) for wp in drone_wps]
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waypoints_gps.append(gps_wps)
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return waypoints_gps, self.current_origin, rendezvous_indices
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# ------------------------------------------------------------------ M-Formation
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def _calculate_m_formation(self, drone_positions, target_point, params):
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params = params or {}
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N = len(drone_positions)
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spacing = params.get('spacing', self.spacing)
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base_altitude = params.get('base_altitude', self.base_altitude)
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altitude_diff = params.get('altitude_diff', self.altitude_diff)
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C_x = sum(pos[0] for pos in drone_positions) / N
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C_y = sum(pos[1] for pos in drone_positions) / N
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T_x, T_y, T_z = target_point
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V_x, V_y = T_x - C_x, T_y - C_y
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P_x, P_y = -V_y, V_x
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length = math.sqrt(P_x ** 2 + P_y ** 2)
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P_x_unit, P_y_unit = (P_x / length, P_y / length) if length > 0.01 else (1.0, 0.0)
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projections = [((pos[0] - C_x) * P_x_unit + (pos[1] - C_y) * P_y_unit, i)
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for i, pos in enumerate(drone_positions)]
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projections.sort()
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stage1_positions = [None] * N
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stage2_positions = [None] * N
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for rank, (_, original_idx) in enumerate(projections):
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offset = (rank - (N - 1) / 2) * spacing
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altitude = base_altitude + (altitude_diff if rank % 2 == 0 else -altitude_diff)
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stage1_positions[original_idx] = (C_x + P_x_unit * offset, C_y + P_y_unit * offset, altitude)
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stage2_positions[original_idx] = (T_x + P_x_unit * offset, T_y + P_y_unit * offset, altitude)
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return stage1_positions, stage2_positions
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# ------------------------------------------------------------------ Circle
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def _calculate_circle_formation(self, drone_positions, target_point, params):
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params = params or {}
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N = len(drone_positions)
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radius = params.get('radius', 10.0)
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altitude = params.get('altitude', 10.0)
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start_angle = params.get('start_angle', 0.0)
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center_x, center_y, center_z = target_point
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stage1_positions = []
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stage2_positions = []
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angle_step = 360.0 / N
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for i in range(N):
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angle_rad = math.radians(start_angle + angle_step * i)
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final_x = center_x + radius * math.cos(angle_rad)
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final_y = center_y + radius * math.sin(angle_rad)
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final_z = altitude
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current_x, current_y, _current_z = drone_positions[i]
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# stage1 高度直接用 final_z：先爬升到目標高度再水平集合，
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# 避免用 current_z 做中點插值（current_z 在實飛時可能是 AMSL 而非相對 home，會導致衝高）
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stage1_positions.append((
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current_x + (final_x - current_x) * 0.5,
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current_y + (final_y - current_y) * 0.5,
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final_z,
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))
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stage2_positions.append((final_x, final_y, final_z))
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return stage1_positions, stage2_positions
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# ------------------------------------------------------------------ 路徑跟隨 (虛擬領隊)
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def _calculate_leader_follower(self, drone_positions, route_wps_local, params):
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"""
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路徑跟隨編隊 — 虛擬領隊 / 弧長參數化
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center_path = 使用者畫的路徑點（直線段連接，不插弧），
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每架 drone 沿弧長 s 參數化做隊形偏移：
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s_follower = s_leader - lon_k
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position = lookup(s_follower) + lat_k × perp(tangent)
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s 超出 path 範圍時外推（不 clip），確保每架 drone 起終點不重疊。
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Rank = 輸入順序（GUI 選取順序），drone_positions[0] = leader。
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隊形 (俯視):
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D0 (lat=-L, lon=0) ← leader
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D1 (lat=+L, lon=5)
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D2 (lat=-L, lon=10)
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D3 (lat=+L, lon=15)
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"""
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N = len(drone_positions)
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lateral_offset = params.get('lateral_offset', 3.0)
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longitudinal_spacing = params.get('longitudinal_spacing', 5.0)
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altitude = params.get('altitude', self.base_altitude)
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# Step 1: 建立中心路徑（直線段連接，每個中間航點設 barrier）
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center_path, barrier_indices = self._build_center_path(route_wps_local)
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s_list = [pt['s'] for pt in center_path]
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s_max = s_list[-1]
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n_pts = len(center_path)
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def lookup(s_target):
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"""在 center path 上以弧長 s 回傳 (x, y, tangent_dir)。超出範圍時沿端點方向外推。"""
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if n_pts == 1:
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pt = center_path[0]
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return pt['x'], pt['y'], pt['dir']
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if s_target <= 0.0:
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a = center_path[0]
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b = center_path[1]
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sdx, sdy = b['x'] - a['x'], b['y'] - a['y']
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slen = math.hypot(sdx, sdy)
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if slen > 1e-6:
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dx, dy = sdx / slen, sdy / slen
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return a['x'] + s_target * dx, a['y'] + s_target * dy, (dx, dy)
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return a['x'], a['y'], a['dir']
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if s_target >= s_max:
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a = center_path[-2]
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b = center_path[-1]
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sdx, sdy = b['x'] - a['x'], b['y'] - a['y']
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slen = math.hypot(sdx, sdy)
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if slen > 1e-6:
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dx, dy = sdx / slen, sdy / slen
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overshoot = s_target - s_max
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return b['x'] + overshoot * dx, b['y'] + overshoot * dy, (dx, dy)
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return b['x'], b['y'], b['dir']
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# Binary search：s_list[lo] <= s_target < s_list[hi]
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lo, hi = 0, n_pts - 1
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while lo + 1 < hi:
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mid = (lo + hi) // 2
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if s_list[mid] <= s_target:
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lo = mid
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else:
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hi = mid
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a = center_path[lo]
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b = center_path[hi]
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ds = s_list[hi] - s_list[lo]
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if ds < 1e-9:
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return a['x'], a['y'], a['dir']
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t = (s_target - s_list[lo]) / ds
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x = a['x'] + t * (b['x'] - a['x'])
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y = a['y'] + t * (b['y'] - a['y'])
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sdx, sdy = b['x'] - a['x'], b['y'] - a['y']
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slen = math.hypot(sdx, sdy)
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if slen > 1e-6:
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return x, y, (sdx / slen, sdy / slen)
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return x, y, a['dir']
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# Step 2: rank = 輸入順序（GUI 選取順序），不再做 projection sort
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# 避免浮點噪音在投影值相近時翻轉 leader，保證 run-to-run 穩定
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all_waypoints = [None] * N
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for rank in range(N):
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lat_sign = -1 if rank % 2 == 0 else 1
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lat = lat_sign * lateral_offset
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lon = rank * longitudinal_spacing
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waypoints = []
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for i in range(n_pts):
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s_follower = s_list[i] - lon
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x, y, (dx, dy) = lookup(s_follower)
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perp_x, perp_y = -dy, dx
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waypoints.append((
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x + lat * perp_x,
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y + lat * perp_y,
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altitude,
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))
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all_waypoints[rank] = waypoints
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return all_waypoints, barrier_indices
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def _build_center_path(self, waypoints):
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"""
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建立中心路徑 — 直接用路徑點連接，不插弧。
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每個中間航點設為 barrier（轉彎集合點），讓隊伍到齊後再推進。
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飛控在 GUIDED 模式下自行處理直線飛行，不需要 GUI 預先平滑。
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Returns:
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(path, barrier_indices)
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"""
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num_wps = len(waypoints)
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if num_wps < 2:
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return [{
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'seg': 'straight',
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'x': waypoints[0][0], 'y': waypoints[0][1],
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'dir': (0.0, 1.0),
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's': 0.0,
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}], []
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path = []
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barrier_indices = []
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for i in range(num_wps):
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if i < num_wps - 1:
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dx = waypoints[i + 1][0] - waypoints[i][0]
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dy = waypoints[i + 1][1] - waypoints[i][1]
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else:
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dx = waypoints[i][0] - waypoints[i - 1][0]
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dy = waypoints[i][1] - waypoints[i - 1][1]
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length = math.hypot(dx, dy)
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if length < 0.01:
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direction = (0.0, 1.0)
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else:
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direction = (dx / length, dy / length)
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path.append({
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'seg': 'straight',
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'x': waypoints[i][0], 'y': waypoints[i][1],
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'dir': direction,
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})
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if 0 < i < num_wps - 1:
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barrier_indices.append(len(path) - 1)
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path[0]['s'] = 0.0
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for i in range(1, len(path)):
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prev = path[i - 1]
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cur = path[i]
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cur['s'] = prev['s'] + math.hypot(
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cur['x'] - prev['x'], cur['y'] - prev['y']
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)
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return path, barrier_indices
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# ------------------------------------------------------------------ 柵狀掃描
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def _calculate_grid_sweep(self, drone_positions, rect_corners_local, params):
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"""柵狀掃描：掃描方向沿矩形長邊，切割方向沿短邊"""
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N = len(drone_positions)
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line_spacing = params.get('line_spacing', 5.0)
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altitude = params.get('altitude', self.base_altitude)
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c0, c1, c2, c3 = rect_corners_local
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edge_a = (c1[0] - c0[0], c1[1] - c0[1])
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len_a = math.sqrt(edge_a[0] ** 2 + edge_a[1] ** 2)
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edge_b = (c3[0] - c0[0], c3[1] - c0[1])
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len_b = math.sqrt(edge_b[0] ** 2 + edge_b[1] ** 2)
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if len_a >= len_b:
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sweep_vec = edge_a
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sweep_len = len_a
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cut_vec = edge_b
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cut_len = len_b
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sweep_start_mid = ((c0[0] + c3[0]) / 2, (c0[1] + c3[1]) / 2)
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sweep_end_mid = ((c1[0] + c2[0]) / 2, (c1[1] + c2[1]) / 2)
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cut_start_corner = c0
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else:
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sweep_vec = edge_b
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sweep_len = len_b
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cut_vec = edge_a
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cut_len = len_a
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sweep_start_mid = ((c0[0] + c1[0]) / 2, (c0[1] + c1[1]) / 2)
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sweep_end_mid = ((c3[0] + c2[0]) / 2, (c3[1] + c2[1]) / 2)
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cut_start_corner = c0
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sweep_dir = (sweep_vec[0] / sweep_len, sweep_vec[1] / sweep_len)
|
||
cut_dir = (cut_vec[0] / cut_len, cut_vec[1] / cut_len)
|
||
|
||
C_x = sum(p[0] for p in drone_positions) / N
|
||
C_y = sum(p[1] for p in drone_positions) / N
|
||
|
||
dist_to_start = math.sqrt(
|
||
(C_x - sweep_start_mid[0]) ** 2 + (C_y - sweep_start_mid[1]) ** 2
|
||
)
|
||
dist_to_end = math.sqrt(
|
||
(C_x - sweep_end_mid[0]) ** 2 + (C_y - sweep_end_mid[1]) ** 2
|
||
)
|
||
|
||
if dist_to_start <= dist_to_end:
|
||
near_corner_a = cut_start_corner
|
||
else:
|
||
sweep_dir = (-sweep_dir[0], -sweep_dir[1])
|
||
near_corner_a = (cut_start_corner[0] + sweep_vec[0],
|
||
cut_start_corner[1] + sweep_vec[1])
|
||
|
||
projections = [
|
||
((pos[0] - C_x) * cut_dir[0] + (pos[1] - C_y) * cut_dir[1], i)
|
||
for i, pos in enumerate(drone_positions)
|
||
]
|
||
projections.sort()
|
||
|
||
strip_width = cut_len / N
|
||
drone_sweep_proj = C_x * sweep_dir[0] + C_y * sweep_dir[1]
|
||
near_sweep_proj = near_corner_a[0] * sweep_dir[0] + near_corner_a[1] * sweep_dir[1]
|
||
gather_sweep_proj = (drone_sweep_proj + near_sweep_proj) / 2
|
||
|
||
all_waypoints = [None] * N
|
||
|
||
for rank, (_, original_idx) in enumerate(projections):
|
||
strip_center_offset = (rank + 0.5) * strip_width
|
||
base_x = near_corner_a[0] + cut_dir[0] * strip_center_offset
|
||
base_y = near_corner_a[1] + cut_dir[1] * strip_center_offset
|
||
|
||
waypoints_list = []
|
||
|
||
gather_offset = gather_sweep_proj - near_sweep_proj
|
||
gx = base_x + sweep_dir[0] * gather_offset
|
||
gy = base_y + sweep_dir[1] * gather_offset
|
||
waypoints_list.append((gx, gy, altitude))
|
||
|
||
num_lines = max(1, int(strip_width / line_spacing))
|
||
actual_spacing = strip_width / num_lines
|
||
first_line_offset = (rank * strip_width) + actual_spacing / 2
|
||
|
||
entry_x = near_corner_a[0] + cut_dir[0] * first_line_offset
|
||
entry_y = near_corner_a[1] + cut_dir[1] * first_line_offset
|
||
waypoints_list.append((entry_x, entry_y, altitude))
|
||
|
||
current_cut_offset = first_line_offset
|
||
|
||
for line_idx in range(num_lines):
|
||
line_near_x = near_corner_a[0] + cut_dir[0] * current_cut_offset
|
||
line_near_y = near_corner_a[1] + cut_dir[1] * current_cut_offset
|
||
line_far_x = line_near_x + sweep_dir[0] * sweep_len
|
||
line_far_y = line_near_y + sweep_dir[1] * sweep_len
|
||
|
||
if line_idx % 2 == 0:
|
||
waypoints_list.append((line_far_x, line_far_y, altitude))
|
||
else:
|
||
waypoints_list.append((line_near_x, line_near_y, altitude))
|
||
|
||
if line_idx < num_lines - 1:
|
||
next_cut_offset = current_cut_offset + actual_spacing
|
||
next_near_x = near_corner_a[0] + cut_dir[0] * next_cut_offset
|
||
next_near_y = near_corner_a[1] + cut_dir[1] * next_cut_offset
|
||
next_far_x = next_near_x + sweep_dir[0] * sweep_len
|
||
next_far_y = next_near_y + sweep_dir[1] * sweep_len
|
||
|
||
if line_idx % 2 == 0:
|
||
waypoints_list.append((next_far_x, next_far_y, altitude))
|
||
else:
|
||
waypoints_list.append((next_near_x, next_near_y, altitude))
|
||
current_cut_offset = next_cut_offset
|
||
|
||
all_waypoints[original_idx] = waypoints_list
|
||
|
||
# 每一條掃描線的「起點」都做一次同步：
|
||
# wp 0 = gather, wp 1 = 第一條線起點, wp 2 = 第一條線終點,
|
||
# wp 3 = 第二條線起點, wp 4 = 第二條線終點, ...
|
||
# 每條線的「起點 index」= 1, 3, 5, ... = 2*i + 1（i 從 0 開始）
|
||
# 所有 drone 的 waypoint 數量相同（num_lines 對所有 strip 都一致），
|
||
# 所以用同一份 rendezvous_indices
|
||
rendezvous_indices = [2 * i + 1 for i in range(num_lines)]
|
||
|
||
return all_waypoints, rendezvous_indices
|
||
|
||
|
||
# ================================================================================
|
||
# 測試
|
||
# ================================================================================
|
||
if __name__ == "__main__":
|
||
planner = FormationPlanner()
|
||
|
||
drones = [
|
||
(24.123450, 120.567800, 0.0),
|
||
(24.123470, 120.567820, 0.0),
|
||
(24.123440, 120.567810, 0.0),
|
||
(24.123460, 120.567830, 0.0),
|
||
]
|
||
target = (24.12400, 120.56795, 10.0)
|
||
|
||
# M-Formation
|
||
wps, origin, rv = planner.plan_formation_mission(drones, target, MissionType.M_FORMATION)
|
||
print("M-Formation:")
|
||
for i, wp_list in enumerate(wps):
|
||
print(f" Drone {i}: {len(wp_list)} waypoints")
|
||
|
||
# Leader-Follower (虛擬領隊 / 弧長參數化)
|
||
route = [
|
||
(24.12345, 120.56780),
|
||
(24.12370, 120.56800),
|
||
(24.12390, 120.56820),
|
||
(24.12400, 120.56800),
|
||
(24.12420, 120.56790),
|
||
]
|
||
wps_lf, origin_lf, rv_lf = planner.plan_formation_mission(
|
||
drones, target, MissionType.LEADER_FOLLOWER,
|
||
params={
|
||
'route_waypoints': route,
|
||
'lateral_offset': 3.0,
|
||
'longitudinal_spacing': 5.0,
|
||
'min_inner_radius': 3.0,
|
||
'arc_resolution': 8,
|
||
'altitude': 10.0
|
||
}
|
||
)
|
||
print(f"\nLeader-Follower (arc-length virtual leader):")
|
||
for i, wp_list in enumerate(wps_lf):
|
||
print(f" Drone {i}: {len(wp_list)} waypoints")
|
||
for j, wp in enumerate(wp_list):
|
||
print(f" WP{j}: ({wp[0]:.6f}, {wp[1]:.6f}, {wp[2]:.1f})")
|
||
|
||
# Grid Sweep
|
||
rect = [
|
||
(24.1237, 120.5679),
|
||
(24.1237, 120.5683),
|
||
(24.1240, 120.5683),
|
||
(24.1240, 120.5679)
|
||
]
|
||
wps2, origin2, rv2 = planner.plan_formation_mission(
|
||
drones, target, MissionType.GRID_SWEEP,
|
||
params={'rect_corners': rect, 'line_spacing': 5.0, 'altitude': 10.0}
|
||
)
|
||
print(f"\nGrid Sweep:")
|
||
for i, wp_list in enumerate(wps2):
|
||
print(f" Drone {i}: {len(wp_list)} waypoints") |