path op• データ種: image2d × matrix → table
• 呼び出し: import fullseye as fs; fs.ledger.car_hybrid_astar(occupancy, poses, radius=1.0, cell=1.0, step=None, n_theta=72, allow_reverse=True, footprint=None, clearance=0.0, reverse_penalty=0.0, switch_penalty=0.0, steer_penalty=0.0, shot_every=None, max_expansions=200000, return_tree=False) (実装を直接呼ぶなら import carpath; carpath.car_hybrid_astar(occupancy, poses, radius=1.0, cell=1.0, step=None, n_theta=72, allow_reverse=True, footprint=None, clearance=0.0, reverse_penalty=0.0, switch_penalty=0.0, steer_penalty=0.0, shot_every=None, max_expansions=200000, return_tree=False)、台帳から引くなら opsgraph.get("car_hybrid_astar"))
占有格子(> 0.5 = 障害物)の上で車の道を Hybrid A*(Dolgov ら 2010)で探す: {左・直進・右} × {前進・後退} の運動基本形を離散セル (x, y, θ) で枝刈りしながら A* で繋ぎ、障害物を無視した Reeds–Shepp 長(前進のみなら Dubins 長)を許容ヒューリスティックに、節点から目標へ解析的な一撃を試す。車体の矩形 footprint か余白 clearance、後退・切替・舵の罰則。姿勢列・長さ・費用・下界(RS 長)・区間・展開数を返す。障害物の無い格子では費用 = RS 長。到達不能は ValueError。
> 以下の詳細説明は原文のままです —— 要約と見出しは訳出済み。
occupancy is a 2-D grid (> 0.5 = obstacle); cell (row, col) covers metres x in [col*cell, (col+1)*cell), y in [row*cell, (row+1)*cell). poses is [[x, y, theta], [x', y', theta']] (start, goal) in metres/radians. The car has minimum turning radius radius; footprint=(length, width, rear_offset) is a rectangle in rear-axle coordinates (None = point car with clearance metres kept from obstacles). Motion primitives are {left, straight, right} × {forward, reverse if allow_reverse} arcs of step metres (default 1.01·sqrt(2)·cell, so each primitive leaves its cell); states are pruned per discrete cell (col, row, theta bin of 2π/n_theta). Cost = path length + reverse_penalty·(reversed length) + switch_penalty per gear change + steer_penalty per steering change (all default 0, so the cost is the geometric length). The heuristic is the obstacle-free Reeds–Shepp length (Dubins when reversing is disallowed), admissible because every penalty is non-negative. At the start node, and then every shot_every expansions (default None = adaptive: a node with cost-to-go h tries when the expansion count is a multiple of ceil(h / step), so nodes near the goal try every time, as in the paper), the shortest analytic path from the node to the goal is collision-checked, and the search ends when that shot is free. On an obstacle-free grid the first shot succeeds and the result equals the Reeds–Shepp (Dubins) length exactly; with obstacles the result is never below it (lower_bound). Cell pruning makes Hybrid A* an approximation of the optimum among all feasible paths, as in the paper.
Returns points (N, 3) poses along the path (start, tree part, analytic shot to the goal), length (metres), cost, lower_bound (Reeds–Shepp/Dubins length start→goal), segments [(kind, gear, length)] of the whole path, n_expanded, n_generated, closed_by_shot (True when the analytic expansion ended the search; False when the goal cell itself was popped), shot_from (pose where the shot started), tree ((K, 3) expanded poses, only when return_tree).
Raises ValueError: grid not 2-D or empty; poses not (2, 3) finite, outside the grid or in collision; radius/cell/step not positive, n_theta < 4, negative penalties; the goal is unreachable (open set exhausted) or max_expansions was hit (reported, never a partial path).
• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。
• poc_car_parking — py -3.11 examples/poc_car_parking.py
• poc_driving_school — py -3.11 examples/poc_driving_school.py
table を入力に取れる)graph_edge_consensus · graph_core_persistence · tree_morphometry · tree_sholl · tree_run_length
path)car_dubins_path · car_reeds_shepp_path
*Provenance: carpath.py — GRAPH operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.