#!/usr/bin/env python3 """ 飛行任務規劃模組 支援: M-Formation, Circle, Leader-Follower (arc-length virtual leader), Grid Sweep """ import math from typing import List, Tuple, Optional, Dict, Any from enum import Enum class MissionType(Enum): """任務類型""" M_FORMATION = "m_formation" CIRCLE_FORMATION = "circle_formation" LEADER_FOLLOWER = "leader_follower" GRID_SWEEP = "grid_sweep" class CoordinateConverter: """GPS 座標與 Local 座標的轉換器""" def __init__(self, origin_lat: float, origin_lon: float, origin_alt: float = 0): self.origin_lat = origin_lat self.origin_lon = origin_lon self.origin_alt = origin_alt self.R = 6371000.0 def gps_to_local(self, lat: float, lon: float, alt: float) -> Tuple[float, float, float]: lat_rad = math.radians(lat) lon_rad = math.radians(lon) origin_lat_rad = math.radians(self.origin_lat) origin_lon_rad = math.radians(self.origin_lon) dlat = lat_rad - origin_lat_rad dlon = lon_rad - origin_lon_rad x = self.R * dlon * math.cos((lat_rad + origin_lat_rad) / 2) y = self.R * dlat z = alt - self.origin_alt return x, y, z def local_to_gps(self, x: float, y: float, z: float) -> Tuple[float, float, float]: origin_lat_rad = math.radians(self.origin_lat) origin_lon_rad = math.radians(self.origin_lon) lat_rad = origin_lat_rad + (y / self.R) lon_rad = origin_lon_rad + (x / (self.R * math.cos((lat_rad + origin_lat_rad) / 2))) lat = math.degrees(lat_rad) lon = math.degrees(lon_rad) alt = self.origin_alt + z return lat, lon, alt class FormationPlanner: """隊形規劃器""" def __init__(self, spacing: float = 5.0, base_altitude: float = 10.0, altitude_diff: float = 2.0): self.spacing = spacing self.base_altitude = base_altitude self.altitude_diff = altitude_diff self.current_origin = None self.converter = None def plan_formation_mission(self, drone_gps_positions: List[Tuple[float, float, float]], target_gps: Tuple[float, float, float], mission_type: MissionType = MissionType.M_FORMATION, params: Optional[Dict[str, Any]] = None ) -> Tuple[List[List[Tuple[float, float, float]]], Tuple[float, float, float], List[int]]: """ 回傳 (waypoints_gps, origin, rendezvous_indices)。 rendezvous_indices：航點序列裡該群組需要等全員到齊才推進的 index 清單。 """ if len(drone_gps_positions) == 0: raise ValueError("無人機位置列表不能為空") center_lat = sum(pos[0] for pos in drone_gps_positions) / len(drone_gps_positions) center_lon = sum(pos[1] for pos in drone_gps_positions) / len(drone_gps_positions) center_alt = sum(pos[2] for pos in drone_gps_positions) / len(drone_gps_positions) self.current_origin = (center_lat, center_lon, center_alt) self.converter = CoordinateConverter(center_lat, center_lon, center_alt) drone_local = [self.converter.gps_to_local(*pos) for pos in drone_gps_positions] target_local = self.converter.gps_to_local(*target_gps) if mission_type == MissionType.M_FORMATION: s1, s2 = self._calculate_m_formation(drone_local, target_local, params) waypoints_local = [[s1[i], s2[i]] for i in range(len(drone_local))] # 起點集合後一起出發到 stage2 rendezvous_indices = [0] elif mission_type == MissionType.CIRCLE_FORMATION: s1, s2 = self._calculate_circle_formation(drone_local, target_local, params) waypoints_local = [[s1[i], s2[i]] for i in range(len(drone_local))] # 半程集合後一起進最終圓環位置 rendezvous_indices = [0] elif mission_type == MissionType.LEADER_FOLLOWER: params = params or {} route_wps_gps = params.get('route_waypoints') if route_wps_gps is None or len(route_wps_gps) < 2: raise ValueError("LEADER_FOLLOWER 需要至少 2 個路徑點") route_wps_local = [ self.converter.gps_to_local(wp[0], wp[1], 0)[:2] for wp in route_wps_gps ] waypoints_local, rendezvous_indices = self._calculate_leader_follower( drone_local, route_wps_local, params ) elif mission_type == MissionType.GRID_SWEEP: params = params or {} rect_corners_gps = params.get('rect_corners') if rect_corners_gps is None or len(rect_corners_gps) != 4: raise ValueError("GRID_SWEEP 需要 4 個 GPS 角點") rect_corners_local = [ self.converter.gps_to_local(c[0], c[1], 0)[:2] for c in rect_corners_gps ] waypoints_local, rendezvous_indices = self._calculate_grid_sweep( drone_local, rect_corners_local, params ) else: raise ValueError(f"不支援的任務類型: {mission_type}") waypoints_gps = [] for drone_wps in waypoints_local: gps_wps = [self.converter.local_to_gps(*wp) for wp in drone_wps] waypoints_gps.append(gps_wps) return waypoints_gps, self.current_origin, rendezvous_indices # ------------------------------------------------------------------ M-Formation def _calculate_m_formation(self, drone_positions, target_point, params): params = params or {} N = len(drone_positions) spacing = params.get('spacing', self.spacing) base_altitude = params.get('base_altitude', self.base_altitude) altitude_diff = params.get('altitude_diff', self.altitude_diff) C_x = sum(pos[0] for pos in drone_positions) / N C_y = sum(pos[1] for pos in drone_positions) / N T_x, T_y, T_z = target_point V_x, V_y = T_x - C_x, T_y - C_y P_x, P_y = -V_y, V_x length = math.sqrt(P_x ** 2 + P_y ** 2) P_x_unit, P_y_unit = (P_x / length, P_y / length) if length > 0.01 else (1.0, 0.0) projections = [((pos[0] - C_x) * P_x_unit + (pos[1] - C_y) * P_y_unit, i) for i, pos in enumerate(drone_positions)] projections.sort() stage1_positions = [None] * N stage2_positions = [None] * N for rank, (_, original_idx) in enumerate(projections): offset = (rank - (N - 1) / 2) * spacing altitude = base_altitude + (altitude_diff if rank % 2 == 0 else -altitude_diff) stage1_positions[original_idx] = (C_x + P_x_unit * offset, C_y + P_y_unit * offset, altitude) stage2_positions[original_idx] = (T_x + P_x_unit * offset, T_y + P_y_unit * offset, altitude) return stage1_positions, stage2_positions # ------------------------------------------------------------------ Circle def _calculate_circle_formation(self, drone_positions, target_point, params): params = params or {} N = len(drone_positions) radius = params.get('radius', 10.0) altitude = params.get('altitude', 10.0) start_angle = params.get('start_angle', 0.0) center_x, center_y, center_z = target_point stage1_positions = [] stage2_positions = [] angle_step = 360.0 / N for i in range(N): angle_rad = math.radians(start_angle + angle_step * i) final_x = center_x + radius * math.cos(angle_rad) final_y = center_y + radius * math.sin(angle_rad) final_z = altitude current_x, current_y, current_z = drone_positions[i] stage1_positions.append(( current_x + (final_x - current_x) * 0.5, current_y + (final_y - current_y) * 0.5, current_z + (final_z - current_z) * 0.5 )) stage2_positions.append((final_x, final_y, final_z)) return stage1_positions, stage2_positions # ------------------------------------------------------------------ 路徑跟隨 (虛擬領隊) def _calculate_leader_follower(self, drone_positions, route_wps_local, params): """ 路徑跟隨編隊 — 虛擬領隊 / 弧長參數化版 (virtual leader / arc-length) 核心想法： - center_path 建好後計算每個 sample 的累積弧長 s - 每架 drone 給 (lat_k, lon_k) 的隊形偏移 - 把 leader 想成沿 s 參數化的點，follower k 的目標 s = s_leader - lon_k - follower k 第 i 個 waypoint = lookup(s_list[i] - lon_k) + lat_k × perp(tangent) - 超過 path 起點/終點以 clip 處理，避免倒退或衝出 解決前版「空間偏移」的三個 bug： 1. 起點暴衝：s<0 clip → follower 起點就是 start 加橫向偏移，不往後倒退 2. 弧段角度爆炸：lon 不再換算成弧度，直接沿 polyline lookup 3. straight/arc 切換不連續：統一走 polyline 線性內插與 segment 切線 Rank 定序規則： **以 drone_positions 的輸入順序為準**（= GUI 選取順序）。 drone_positions[0] = rank 0 = leader (lon=0)， drone_positions[1] = rank 1 (lon=5)，依此類推。 刻意「不」用位置投影自動排序，避免浮點噪音造成 run-to-run leader 漂移，以確保整個 mission 像軍隊縱隊那樣順序固定。 隊形 (俯視, 以路徑行進方向為前): D0 (lat=-L, lon=0) ← front-right (leader) D1 (lat=+L, lon=5) D2 (lat=-L, lon=10) D3 (lat=+L, lon=15) """ N = len(drone_positions) lateral_offset = params.get('lateral_offset', 3.0) longitudinal_spacing = params.get('longitudinal_spacing', 5.0) altitude = params.get('altitude', self.base_altitude) min_inner_radius = params.get('min_inner_radius', 3.0) arc_resolution = params.get('arc_resolution', 12) sharp_turn_cos = params.get('sharp_turn_cos_threshold', 0.0) # cos(90°)=0 # Step 1: 建立中心路徑（含圓弧、銳角單點；每點帶累積 s） center_path, barrier_indices = self._build_center_path( route_wps_local, formation_max_lateral=lateral_offset, min_inner_radius=min_inner_radius, arc_resolution=arc_resolution, sharp_turn_cos_threshold=sharp_turn_cos, ) s_list = [pt['s'] for pt in center_path] s_max = s_list[-1] n_pts = len(center_path) def lookup(s_target): """ 在 center path 上以弧長 s 回傳 (x, y, tangent_dir)。 s<0 → clip 到 start；s>s_max → clip 到 end。 tangent 方向取當前 polyline segment 的切線，避免對不連續的 dir 做插值。 """ if n_pts == 1: pt = center_path[0] return pt['x'], pt['y'], pt['dir'] if s_target <= 0.0: a = center_path[0] b = center_path[1] sdx, sdy = b['x'] - a['x'], b['y'] - a['y'] slen = math.hypot(sdx, sdy) if slen > 1e-6: return a['x'], a['y'], (sdx / slen, sdy / slen) return a['x'], a['y'], a['dir'] if s_target >= s_max: a = center_path[-2] b = center_path[-1] sdx, sdy = b['x'] - a['x'], b['y'] - a['y'] slen = math.hypot(sdx, sdy) if slen > 1e-6: return b['x'], b['y'], (sdx / slen, sdy / slen) return b['x'], b['y'], b['dir'] # Binary search：s_list[lo] <= s_target < s_list[hi] lo, hi = 0, n_pts - 1 while lo + 1 < hi: mid = (lo + hi) // 2 if s_list[mid] <= s_target: lo = mid else: hi = mid a = center_path[lo] b = center_path[hi] ds = s_list[hi] - s_list[lo] if ds < 1e-9: return a['x'], a['y'], a['dir'] t = (s_target - s_list[lo]) / ds x = a['x'] + t * (b['x'] - a['x']) y = a['y'] + t * (b['y'] - a['y']) sdx, sdy = b['x'] - a['x'], b['y'] - a['y'] slen = math.hypot(sdx, sdy) if slen > 1e-6: return x, y, (sdx / slen, sdy / slen) return x, y, a['dir'] # Step 2: rank = 輸入順序（GUI 選取順序），不再做 projection sort # 避免浮點噪音在投影值相近時翻轉 leader，保證 run-to-run 穩定 all_waypoints = [None] * N # 預先算好路徑終點的切線（給收尾點用） end = center_path[-1] end_dx, end_dy = end['dir'] if n_pts >= 2: a = center_path[-2] sdx, sdy = end['x'] - a['x'], end['y'] - a['y'] slen = math.hypot(sdx, sdy) if slen > 1e-6: end_dx, end_dy = sdx / slen, sdy / slen for rank in range(N): lat_sign = -1 if rank % 2 == 0 else 1 lat = lat_sign * lateral_offset lon = rank * longitudinal_spacing waypoints = [] for i in range(n_pts): s_follower = s_list[i] - lon x, y, (dx, dy) = lookup(s_follower) perp_x, perp_y = -dy, dx waypoints.append(( x + lat * perp_x, y + lat * perp_y, altitude, )) # 收尾：全員到 path 終點的 rank 位置（lon 歸零） waypoints.append(( end['x'] + lat * (-end_dy), end['y'] + lat * end_dx, altitude, )) all_waypoints[rank] = waypoints # barrier 索引仍指向 center_path 內 (range [0, n_pts-1])， # 不觸及末尾收尾點，直接沿用即可 return all_waypoints, barrier_indices def _build_center_path(self, waypoints, formation_max_lateral, min_inner_radius, arc_resolution, sharp_turn_cos_threshold): """ 建立以「圓弧」連接直線段的中心路徑。 每個中間 waypoint 的處理： 1. 計算入向 u_in、出向 u_out 的夾角 β = acos(u_in·u_out) 2. 若 cos β < sharp_turn_cos_threshold → 銳角：只插單點 (hover + 原地轉向)， barrier 放在該點，讓隊伍整體停下再繼續 3. 否則 → 平滑弧： R_base = formation_max_lateral + min_inner_radius d = R_base × tan(β/2)，並以相鄰段長的 45% 為上限 R_actual = d / tan(β/2) tangent points T_in = WP - u_in·d, T_out = WP + u_out·d arc center = T_in + R_actual·sign·n_left，其中 sign=+1 左轉/CCW，-1 右轉/CW arc 由 theta_in 到 theta_out 以 arc_resolution 等分 path 內容: [T_in(straight,u_in), arc_samples..., T_out(straight,u_out)] barrier 放在 T_out (轉完彎後的集合點) path 是一個 list[dict]，每個 dict 至少含: {'seg': 'straight' | 'arc', 'x': float, 'y': float, 'dir': (dx, dy)} 'arc' 類型額外含: 'arc_center', 'arc_R', 'arc_sign', 'theta' Returns: (path, barrier_indices) """ num_wps = len(waypoints) if num_wps < 2: return [{ 'seg': 'straight', 'x': waypoints[0][0], 'y': waypoints[0][1], 'dir': (0.0, 1.0), }], [] segments = [] for i in range(num_wps - 1): dx = waypoints[i + 1][0] - waypoints[i][0] dy = waypoints[i + 1][1] - waypoints[i][1] length = math.hypot(dx, dy) if length < 0.01: segments.append(((0.0, 1.0), length)) else: segments.append(((dx / length, dy / length), length)) R_base = formation_max_lateral + min_inner_radius path = [] barrier_indices = [] # 起點 path.append({ 'seg': 'straight', 'x': waypoints[0][0], 'y': waypoints[0][1], 'dir': segments[0][0], }) for i in range(1, num_wps - 1): u_in, len_in = segments[i - 1] u_out, len_out = segments[i] wp = waypoints[i] dot = u_in[0] * u_out[0] + u_in[1] * u_out[1] dot = max(-1.0, min(1.0, dot)) # 銳角：hover + 原地轉向 if dot < sharp_turn_cos_threshold: avg_dx = u_in[0] + u_out[0] avg_dy = u_in[1] + u_out[1] avg_len = math.hypot(avg_dx, avg_dy) if avg_len > 0.01: avg_dir = (avg_dx / avg_len, avg_dy / avg_len) else: avg_dir = u_in path.append({ 'seg': 'straight', 'x': wp[0], 'y': wp[1], 'dir': avg_dir, }) barrier_indices.append(len(path) - 1) continue # 平滑弧 beta = math.acos(dot) if beta < 1e-4: # 幾乎直線，不插弧 path.append({ 'seg': 'straight', 'x': wp[0], 'y': wp[1], 'dir': u_in, }) continue half_tan = math.tan(beta / 2.0) d_ideal = R_base * half_tan d_max = 0.45 * min(len_in, len_out) d = min(d_ideal, d_max) R_actual = d / half_tan T_in = (wp[0] - u_in[0] * d, wp[1] - u_in[1] * d) T_out = (wp[0] + u_out[0] * d, wp[1] + u_out[1] * d) # +1 CCW (左轉) / -1 CW (右轉) cross = u_in[0] * u_out[1] - u_in[1] * u_out[0] sign = 1 if cross > 0 else -1 # 入向左法線 n_left = (-u_in[1], u_in[0]) center = ( T_in[0] + R_actual * sign * n_left[0], T_in[1] + R_actual * sign * n_left[1], ) theta_in = math.atan2(T_in[1] - center[1], T_in[0] - center[0]) # T_in (straight, 方向 u_in) path.append({ 'seg': 'straight', 'x': T_in[0], 'y': T_in[1], 'dir': u_in, }) # 弧段採樣 k=1..arc_resolution-1（不含兩端） for k in range(1, arc_resolution): t = k / arc_resolution theta = theta_in + sign * beta * t px = center[0] + R_actual * math.cos(theta) py = center[1] + R_actual * math.sin(theta) # 切線: sign × (-sin θ, cos θ) tx = sign * (-math.sin(theta)) ty = sign * math.cos(theta) path.append({ 'seg': 'arc', 'x': px, 'y': py, 'dir': (tx, ty), 'arc_center': center, 'arc_R': R_actual, 'arc_sign': sign, 'theta': theta, }) # T_out (straight, 方向 u_out)，barrier 放這 path.append({ 'seg': 'straight', 'x': T_out[0], 'y': T_out[1], 'dir': u_out, }) barrier_indices.append(len(path) - 1) # 終點 path.append({ 'seg': 'straight', 'x': waypoints[-1][0], 'y': waypoints[-1][1], 'dir': segments[-1][0], }) # 後處理：為每個 sample 加上累積 polyline 弧長 s path[0]['s'] = 0.0 for i in range(1, len(path)): prev = path[i - 1] cur = path[i] cur['s'] = prev['s'] + math.hypot( cur['x'] - prev['x'], cur['y'] - prev['y'] ) return path, barrier_indices # ------------------------------------------------------------------ 柵狀掃描 def _calculate_grid_sweep(self, drone_positions, rect_corners_local, params): """柵狀掃描：掃描方向沿矩形長邊，切割方向沿短邊""" N = len(drone_positions) line_spacing = params.get('line_spacing', 5.0) altitude = params.get('altitude', self.base_altitude) c0, c1, c2, c3 = rect_corners_local edge_a = (c1[0] - c0[0], c1[1] - c0[1]) len_a = math.sqrt(edge_a[0] ** 2 + edge_a[1] ** 2) edge_b = (c3[0] - c0[0], c3[1] - c0[1]) len_b = math.sqrt(edge_b[0] ** 2 + edge_b[1] ** 2) if len_a >= len_b: sweep_vec = edge_a sweep_len = len_a cut_vec = edge_b cut_len = len_b sweep_start_mid = ((c0[0] + c3[0]) / 2, (c0[1] + c3[1]) / 2) sweep_end_mid = ((c1[0] + c2[0]) / 2, (c1[1] + c2[1]) / 2) cut_start_corner = c0 else: sweep_vec = edge_b sweep_len = len_b cut_vec = edge_a cut_len = len_a sweep_start_mid = ((c0[0] + c1[0]) / 2, (c0[1] + c1[1]) / 2) sweep_end_mid = ((c3[0] + c2[0]) / 2, (c3[1] + c2[1]) / 2) cut_start_corner = c0 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")