agv op• Data kinds: image2d → table
• Call: import fullseye as fs; fs.ledger.mapf_cbs(grid, starts, goals, max_nodes=20000, horizon=None) (to call the implementation directly, import agvfleet; agvfleet.mapf_cbs(grid, starts, goals, max_nodes=20000, horizon=None); from the registry, opsdrive.get("mapf_cbs"))
Conflict-Based Search (Sharon, Stern, Felner, Sturtevant 2015). A collision-free plan with optimal sum of costs.
The upper level is a best-first search over a binary tree that "picks one conflict and adds a constraint to one of the two vehicles"; the lower level is a single-vehicle space-time A*. Returns a dict: paths (sequence of cells for each vehicle), cost (sum of costs), nodes (number of expanded nodes), solved. When the node limit is reached, solved=False (it does not silently return a non-optimal solution).
• 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_ecbs · mapf_joint_astar · mapf_prioritized · plan_conflicts · plan_cost · adg_build
*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.