Autonomous Mobile Robot Navigation AI-Powered AMR System

Project Overview

Developed a comprehensive autonomous navigation system for industrial AMRs operating in dynamic warehouse environments. The system combines computer vision, sensor fusion, and machine learning to enable safe autonomous operation in shared human-robot workspaces.

The Challenge

A logistics company needed autonomous mobile robots for warehouse inventory management and delivery operations. Key challenges included:

• Navigation in cluttered warehouse aisles
• Real-time obstacle detection (forklifts, humans, pallets)
• Dynamic path planning in changing layouts
• Precise localization without GPS
• Integration with Warehouse Management Systems (WMS)
• Compliance with ISO 3691-4 safety standards

Our Solution

1. Multi-Sensor Fusion System

Integrated cameras, LiDAR, IMU, and ultrasonic sensors to create a comprehensive perception system. The fusion algorithm provides robust 3D mapping and localization even in challenging lighting conditions.

2. Real-time Obstacle Detection

Developed a lightweight YOLOv7 model optimized for edge deployment that detects and classifies obstacles in real-time. The system can identify people, vehicles, structures, and dynamic objects with 95% accuracy.

3. Dynamic Path Planning

Implemented an adaptive path planning algorithm that combines A* for global planning with Dynamic Window Approach for local obstacle avoidance. The system recalculates paths in real-time based on changing environments.

4. SLAM Integration

Integrated Simultaneous Localization and Mapping (SLAM) for navigation in GPS-denied warehouses. The system builds and maintains 2D/3D maps while tracking the robot's position with centimeter-level accuracy using LiDAR and wheel odometry.

Technical Implementation

Perception Pipeline

The perception system processes multiple sensor inputs:

• Visual Processing: RGB and depth image analysis for obstacle detection
• LiDAR Processing: Point cloud analysis for 3D mapping and obstacle detection
• Sensor Fusion: Kalman filtering for robust state estimation
• Object Tracking: Multi-object tracking for dynamic obstacle avoidance

Navigation Architecture

The navigation system consists of:

• Global path planner for facility-wide navigation
• Local path planner for dynamic obstacle avoidance (TEB/DWA)
• Kinematic controller for smooth motion profiles
• Safety-rated stop system

Results & Impact

Performance Metrics

• Successfully completed 5,000+ km of autonomous travel
• Reduced material handling costs by 40%
• Achieved 99.0% collision avoidance in mixed traffic
• Maintained 2cm docking accuracy
• Processed obstacle detection in under 15ms

Safety Features

The system includes comprehensive safety mechanisms:

• Emergency Stop: Hardware-level e-stop for immediate halting
• Safety Zones: Dynamic speed reduction in high-traffic areas
• Battery Management: Autonomous charging when battery is low
• Load Stability: Active monitoring of payload stability
• Manual Override: Joystick control for manual maneuvering

Applications

The autonomous navigation system enables various applications:

• Intralogistics: Automated material transport in factories
• E-commerce Fulfillment: Goods-to-person picking systems
• Hospital Logistics: Autonomous delivery of medicine and linens
• Retail Inventory: Shelf scanning and stock monitoring
• Disinfection: Autonomous UV-C disinfection robots

Future Enhancements

Planned improvements include:

• Fleet management for multi-robot coordination
• Advanced semantic mapping for better context understanding
• Integration with 5G for low-latency cloud processing
• Reinforcement learning for optimized path planning
• Opportunity charging optimization