mapf_ecbs — DRIVE `agv` op

• Data kinds: image2d → table

• Call: import fullseye as fs; fs.ledger.mapf_ecbs(grid, starts, goals, w=1.2, max_nodes=50000, horizon=None) (to call the implementation directly, import agvfleet; agvfleet.mapf_ecbs(grid, starts, goals, w=1.2, max_nodes=50000, horizon=None); from the registry, opsdrive.get("mapf_ecbs"))

Usage

CBS with focal search at the high level (the same idea as the high level of ECBS, Barer et al. 2014). Sum of costs is within w times the optimum.

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

開いている節のうち最小の総コスト LB は最適値以下(最適解はどれかの節の下にある)。そこで コスト ≤ w · LB の節(焦点)の中から衝突の少ないものを展開する。返す計画は cost ≤ w · LB ≤ w · 最適(門)。返りは mapf_cbs の dict に lower_bound と w を足したもの。細い通路で最適の CBS が同じ長さの譲り合いの組を全部調べて止まるのを、衝突の少ない側へ寄せて抜ける。

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_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).*

mapf_ecbs — DRIVE agv op