DecodeX
Home/Problem Statements/SIH26123
All 226 Statements
SIH26123SoftwareSmart Automation
₹1,50,000 Prize (₹75k Joint)
•
Deadline: 20 September 2026
•
Submitted Ideas: 0/500

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

Ministry / Organization: Bharat Electronics Limited
SIH Official Portal
Technical Complexity
8/10
Dataset Accessibility
7/10
36h Hackathon Viability
9/10
Step 0 • Zero-Jargon Layman Summary

“We are building a smart traffic control system for warehouse robots so they can talk to each other and navigate without crashing. Instead of relying on a central computer that might fail, each robot makes its own decisions to avoid jams and finish tasks faster.”

The DecodeX PS Decoder™

Deconstructed Problem Blueprint

Step 1

WHAT TO BUILD?

A decentralized multi-agent coordination software framework designed to run on edge hardware (Jetson/Raspberry Pi) for autonomous mobile robots.

Step 2

WHO IS IT FOR?

Bharat Electronics Limited (BEL) and large-scale warehouse operators, logistics managers, and automated manufacturing facilities.

Step 3

WHY DOES IT EXIST?

Centralized systems create single points of failure and latency issues. In a warehouse, if the Wi-Fi drops or the server lags, the entire fleet stops, leading to massive productivity losses.

Step 4

INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE

01 • Inputs & Telemetry

Lidar/Depth camera point clouds, IMU sensor data, peer-to-peer Wi-Fi/Bluetooth signals, and task priority queues.

02 • Processing & AI Core

Distributed consensus algorithms (e.g., Raft or Paxos-lite), Collision Avoidance (ORCA or Velocity Obstacles), and decentralized task allocation (Market-based bidding).

03 • Actionable Deliverables

Real-time velocity commands for robot motors, collision-free path trajectories, and a lightweight dashboard visualizing fleet health and status.

Step 5

CONSTRAINTS

  • Must run on edge hardware (limited CPU/RAM)
  • Zero reliance on a central server for pathfinding
  • Must handle dynamic obstacles (human workers/other robots)
  • Low-latency communication requirement
Step 6

JURY METRICS

  • Zero inter-robot collisions
  • 20% reduction in task completion time vs. stop-and-wait
  • Communication overhead per robot
  • System recovery time after node failure
Step 7

CORE BOTTLENECK

Achieving stable 'Distributed Consensus'—ensuring all robots agree on who moves first at a narrow intersection without a central 'traffic light' controller.

Why You Should Choose This PS (Pros)

  • High industrial relevance for BEL
  • Clear, measurable success criteria
  • Strong potential for hardware-software integration demos
  • Highly scalable concept

Challenges & Risks to Watch (Cons)

  • Requires strong grasp of robotics middleware (ROS 2)
  • Simulation environment setup can be time-consuming
  • Debugging distributed systems is notoriously difficult
Hackathon Blueprint

Recommended Tech Stack & Grand Finale Features

Frontend & Mobile
React.jsThree.js for 3D visualizationTailwind CSS
Backend & APIs
PythonROS 2 (Humble/Jazzy)FastAPI for dashboard API
AI / ML & Models
ORCA (Optimal Reciprocal Collision Avoidance)A* or D* Lite for path planning
Database & Cloud
Redis (for local state sharing)SQLite for logging

Killer Features to Impress the SIH Jury

Self-healing network: Robots automatically re-elect a leader if one fails
Predictive intent sharing: Robots broadcast their 'planned path' to others to prevent future collisions
Energy-aware routing: Robots with low battery are prioritized for shorter paths
View Official SIH 2026 Portal Raw Text
▼
•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.