30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
from __future__ import annotations
|
|
|
|
import asyncio
|
|
from dataclasses import dataclass, field
|
|
from typing import Dict, Any
|
|
|
|
from .models import LocalRobotPlan, PlanDelta, SharedSignals, DualVariables
|
|
|
|
|
|
@dataclass
|
|
class SolverState:
|
|
duals: Dict[str, DualVariables] = field(default_factory=dict)
|
|
deltas: Dict[str, PlanDelta] = field(default_factory=dict)
|
|
|
|
|
|
async def admm_step(left_plan: LocalRobotPlan, right_plan: LocalRobotPlan, signals: SharedSignals) -> PlanDelta:
|
|
# Very small, toy ADMM-like step: adjust a simple objective value per fleet and produce delta
|
|
# This is a placeholder for a real, more complex asynchronous coordination loop.
|
|
await asyncio.sleep(0.01) # simulate async work
|
|
delta_changes = {
|
|
"cost_improvement": max(0.0, 1.0 - signals.signals.get("energy", 0.0))
|
|
}
|
|
return PlanDelta(delta_id=f"delta-{left_plan.robot_id}-{right_plan.robot_id}", fleet_id=left_plan.fleet_id, changes=delta_changes)
|
|
|
|
|
|
async def coordinate_fleets(left_plan: LocalRobotPlan, right_plan: LocalRobotPlan, registry_signals: SharedSignals) -> PlanDelta:
|
|
# Run a single ADMM-like step between two fleets.
|
|
delta = await admm_step(left_plan, right_plan, registry_signals)
|
|
return delta
|