adg_build — DRIVE `agv` op

• Data kinds: table → table

• Call: import fullseye as fs; fs.ledger.adg_build(paths) (to call the implementation directly, import agvfleet; agvfleet.adg_build(paths); from the registry, opsdrive.get("adg_build"))

Usage

Action dependency graph (Hönig, Kiesel, Tinka, Durham, Ayanian 2019).

> The detailed description below is the original text — the summary and the headings are translated.

各車両の行動 = 「訪れるマスの列の k 番目に入る」。依存は 2 種類:

(1) 同じ車両の前の行動、(2) 同じマスを先に使う他車がそのマスを出ること(計画の時刻順)。

返りは dict: cells(各車両のマスの列)、deps((i, k) → それより先に終わるべき (j, m) の集合)。計画に衝突があれば ValueError(依存の向きが決まらない)。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_agv_fleet — py -3.11 examples/poc_agv_fleet.py

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`agv`)

warehouse_grid · grid_distances · mapf_cbs · mapf_ecbs · mapf_joint_astar · mapf_prioritized · plan_conflicts · plan_cost

*Provenance: agvfleet.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

adg_build — DRIVE agv op