Autonomous service fleets—retail carts, clinical pushchairs, airport trolleys—must route continuously through crowded public space, where classical planners treat pedestrians as physical obstacles rather than people whose personal space carries a cost. We propose Distributed Dynamic Route Optimization (D²RO), powered by Socially-Weighted Distributed Graph Optimization (SW-DGO): a multi-objective cost field minimised subject to directional corridor reservations and solved via incremental D* Lite graph repair over V2V mesh telemetry.
Across 4,650 trials in supermarket, hospital, and airport topologies, proxemic routing eliminates intimate-space exposure (reducing median exposure to 0.00 person-seconds). Controlled experiments establish when distributed coordination is warranted: mesh telemetry and corridor reservations earn their cost under hidden blockages and single-file bottlenecks, advancing rerouting by 11.1 s and raising corridor mission success from 36% to 88%.
github.com/polla-fattah/SW-DGO — source, datasets and tests
Three environments with live ablation toggles. Runs the actual Python planner compiled to WebAssembly (Pyodide), not a re-implementation.
d2ro/)
D* Lite replanner, directed cost graph, proxemics field, V2V mesh, reservation protocol, four baselines, three environments.
Raw CSVs from 4,650 trials across eleven experiments. Each file carries a fingerprint of the code that produced it.
Turns the raw CSVs into every reported statistic. Refuses to report data whose source code has since changed.
67 tests: replanner optimality against Dijkstra, heuristic admissibility, kinematics, metric semantics, provenance.
Repository layout, reproduction commands, limitations, future work.
Classical planners fail here in two specific ways. A shortest-path planner drives through a browsing shopper's personal space because nothing in its objective says not to, and reactive avoidance only engages once someone is already close — by which point the intrusion has happened. Separately, two carts entering one single-file aisle from opposite ends is not a collision-avoidance problem but a coordination problem, and a purely local controller can only oscillate.
| Planner | Success | Makespan (s) | Exposure (person-s, median) |
|---|---|---|---|
| SW-DGO Proposed | 99.0% | 47.18 ± 13.40 | 0.00 |
| Local Social D* Lite | 100.0% | 39.06 ± 15.12 | 0.00 |
| Static A* (matched controller) | 100.0% | 19.20 | 6.40 |
| Static A* | 100.0% | 18.00 | 6.40 |
| Artificial potential fields | 100.0% | 34.54 | 10.18 |
The table above is the broad randomized benchmark, and the proposed system does not win it. Local Social D* Lite — the same proxemic routing with no mesh and no reservation — matches its social compliance exactly and finishes about 8 s sooner. That 8 s is the overhead of a distributed layer whose target topologies a randomized supermarket run rarely produces.
The two experiments below construct those topologies deliberately. Both are N = 50 seed-paired trials with the mechanism the only thing switched.
| Controlled mechanism experiment | Mechanism ON | OFF | p |
|---|---|---|---|
| Anticipation lead time (s) V2V mesh | 10.70 ± 4.20 | −0.10 ± 0.04 | 3.8 × 10−9 |
| Backtracking distance (m) | 1.08 ± 0.68 | 2.73 ± 0.87 | 3.2 × 10−8 |
| Corridor mission success Reservation | 88.0% | 36.0% | 1.7 × 10−4 |
| Off-corridor vertices (diversion) | 2.16 ± 1.36 | 0.00 | 2.3 × 10−8 |
Read together, the two tables give the actual finding, which is conditional rather than promotional: the distributed layer is not a general improvement but a targeted one. A deployment whose topology routinely produces blockages beyond sensing range, or single-file corridors entered from both ends, should pay for the radio. One that does not should run the local planner — simpler, faster, and equally well behaved around people.
The reservation also turns out not to work the way its name suggests. Head-on encounters are statistically unchanged and total waiting is 0.03 s: agents do not queue for the corridor, they leave it. We therefore describe it as cost-projected diversion rather than mutual exclusion.
A 2×2 factorial with the mesh and reservation disabled in every cell isolates the proxemic cost term: enabling it alone reduces exposure by 6.40 person-seconds (95% CI [−6.48, −6.31], p = 1.5 × 10−9), at a cost of 19.03 s of travel time. Reactive yielding adds no statistically detectable reduction on top of it (p = 0.59).
The distributed layer is conditional. An ordinary human-aware planner with no mesh and no reservation matches that social compliance exactly (p = 1) while finishing about 8 s sooner. The mesh and reservation earn their cost only under the topologies they were designed for — a blockage outside a follower's sensing range, and a single-file corridor entered from both ends.