Edge-AI Based Distributed Fleet Coordination for Autonomous Mobile Robots (AMRs) in Smart Warehouses

• Background Modern smart warehouses rely on fleets of Autonomous Mobile Robots (AMRs) to move goods efficiently. As fleet sizes grow, relying entirely on a centralized cloud server for path planning causes high network latency, Wi-Fi dead-zone vulnerabilities, and single-point-of-failure risks.To ensure continuous operation, modern robotics is shifting toward decentralized, edge-computing solutions where robots can talk to each other directly and make split-second decisions on the fly.
• Description The objective is to design a decentralized coordination and collision-avoidance framework for a multi-robot fleet (at least 3 AMRs) operating in a dynamic warehouse environment. The system must run locally on edge hardware (e.g., Raspberry Pi or Jetson Nano onboard each robot) and handle:

1. Decentralized Communication: Inter-robot messaging to share position and intent without a central server.

2. Dynamic Multi-Agent Conflict Resolution: Resolving deadlocks and avoiding collisions at narrow intersections or choke points in real-time.

3. Task Allocation & Re-routing: Automatically re-assigning pickup points or changing paths if one robot encounters a blocked aisle.

• Expected Solution A multi-robot simulation featuring:
• Decentralized Network Stack: A peer-to-peer communication protocol where robots share localization data locally.
• Multi-Agent Path Planning: Implementation of algorithms for edge hardware.
• Fleet Dashboard: A lightweight monitoring UI that visualizes the entire fleet's real-time positions and battery status.
• Success Criteria: Zero inter-robot collisions and a minimum 20% reduction in total task completion time compared to traditional stop-and-wait methods when handling overlapping paths.