mapf_joint_astar — DRIVE `agv` op

• Data kinds: image2d → table

• Call: import fullseye as fs; fs.ledger.mapf_joint_astar(grid, starts, goals, max_expansions=2000000) (to call the implementation directly, import agvfleet; agvfleet.mapf_joint_astar(grid, starts, goals, max_expansions=2000000); from the registry, opsdrive.get("mapf_joint_astar"))

Usage

A* with the positions of all vehicles as 1 single state. Optimal solution for small problems only (the ground truth for the CBS gate).

To count the sum of costs exactly, each vehicle carries a "finished (stays at its goal from now on)" flag:

a vehicle at its goal may finish before that move, and the cost of one move = the number of vehicles not yet finished. Heuristic = sum of BFS distances of the unfinished vehicles (admissible).

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_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).*

mapf_joint_astar — DRIVE agv op