agv op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.naive_execute(paths, delay_prob=0.3, seed=0, max_steps=10000) (to call the implementation directly, import agvfleet; agvfleet.naive_execute(paths, delay_prob=0.3, seed=0, max_steps=10000); from the registry, opsdrive.get("naive_execute"))
Naive execution for comparison: uses only the plan's sequence of cells and advances whenever the next cell is free (no ordering agreement).
Once delays are introduced, 2 vehicles that per the plan should pass each other with a time offset end up stopped facing each other. The return value has the same form as adg_execute plus waits_for (the edges of "who waits for whom" at the moment of deadlock).
Empty input (paths with 0 entries) raises ValueError.
• 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.
• poc_agv_fleet — py -3.11 examples/poc_agv_fleet.py
table as input)course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.