path op• 資料種類:image2d × matrix → table
• 呼叫: 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)(要直接呼叫實作,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);從台帳取用則 opsgraph.get("car_hybrid_astar"))
> 該運算子的說明尚無譯文,以下照原文給出。
Hybrid A* (Dolgov, Thrun, Montemerlo and Diebel 2010): a car's path through an occupancy grid.
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).
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• poc_car_parking — py -3.11 examples/poc_car_parking.py
• poc_driving_school — py -3.11 examples/poc_driving_school.py
table 作為輸入)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 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.