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:
Decentralized Communication: Inter-robot messaging to share position and intent without a central server.
Dynamic Multi-Agent Conflict Resolution: Resolving deadlocks and avoiding collisions at narrow intersections or choke points in real-time.
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.

Source: this problem statement was published by Bharat Electronics Limited and is reproduced here in full, unedited. Aggregation and presentation by E-Cell, Amrita Vishwa Vidyapeetham, Nagercoil Campus.