Markster FleetMind AI - Robot Fleet Orchestration

Markster FleetMind AI is a simulation-first platform for orchestrating a warehouse robot fleet. It acts as the centralized backend for planning, coordination, and operations, and exposes a real-time web dashboard for operators. Problem: As fleets scale, ops teams fight congestion, charging bottlenecks, and brittle vendor-specific tooling. Small changes can break throughput and there is rarely a safe place to test policies. Solution: FleetMind runs an orchestration loop that (1) ingests or generates tasks, (2) assigns tasks to the nearest capable robot based on priority, (3) routes robots through aisles using A* pathfinding with collision avoidance, and (4) manages battery and charging behavior. The dashboard streams live robot state, tasks, KPIs (throughput, utilization, queue depth), and alerts via WebSocket. An optional Gemini integration produces concise operational insights and can re-prioritize the queued task list. Deployment: The backend and web app are deployed on a Vultr VM and are accessible in any browser. The current MVP is simulation-first (robots are virtual); the next step is ROS2/vendor connectors so the same control plane can run against real fleets.