path op• Datenarten: image2d × matrix → table
• Aufruf: 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) (die Implementierung direkt: 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); aus dem Register: opsgraph.get("car_hybrid_astar"))
> Für diesen Operator gibt es noch keine Übersetzung. Es folgt der Originaltext unverändert.
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).
• Katalog der Beispieldaten (Download-URLs / Lizenzen) — 2-D nutzt skimage.data (BSD/Public Domain) plus synthetische Bilder, 3-D nennt Download-URLs echter Datenquellen (Stanford, PDS, …).
• Herkunft und Literatur der Operatoren — die Quellen der Forschung/Verfahren, auf denen diese Operatorfamilie beruht.
• Der kanonische Algorithmus (Autor, Jahr) und seine Anwendungen stehen im Familienleitfaden oben.
• poc_car_parking — py -3.11 examples/poc_car_parking.py
• poc_driving_school — py -3.11 examples/poc_driving_school.py
table als Eingabe)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. Diese Notiz wird von tools/opdocs.py md erzeugt (nicht von Hand bearbeiten).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.