Logistics / Warehousing / Robotics

|Major E-commerce Logistics Provider|

16 months

12 engineers

Edge AI Robotics: Autonomous Mobile Robots Increasing Warehouse Throughput by 340% with TinyML Navigation

Q: What were the key results?

The AMR deployment transformed warehouse operations, achieving dramatic improvements in throughput, accuracy, and operational efficiency within 4 months of full deployment.

Q: What technologies were used in this Logistics / Warehousing / Robotics project?

Technologies used: ROS2, TensorFlow Lite, Cortex-A78, Custom NPU, 3D LiDAR, Visual SLAM, 802.11s Mesh, MQTT, PostgreSQL, Redis, Kubernetes, React, Grafana

Design and deployment of a fleet of 200+ autonomous mobile robots (AMRs) with on-device TinyML navigation and coordination, transforming a traditional warehouse into a fully automated fulfillment center with 340% throughput improvement.

340%

Increase in Warehouse Throughput

200+

Autonomous Robots Deployed

99.2%

Order Accuracy Rate

78%

Reduction in Pick Time

Edge AI Robotics: Autonomous Mobile Robots Increasing Warehouse Throughput by 340% with TinyML Navigation - Rapid Circuitry embedded systems case study hero image

The Challenge

A leading e-commerce logistics provider operating a 500,000 sq ft fulfillment center needed to dramatically increase throughput capacity to handle 3x order volume growth while maintaining accuracy and reducing operational costs in a tight labor market.

Labor Shortage

The warehouse struggled to hire and retain workers for repetitive picking tasks. High turnover rates and rising wages created operational instability and unpredictable fulfillment capacity.

Impact: 40% annual turnover, 25% positions unfilled

Limited Throughput

Manual picking processes created bottlenecks during peak seasons. Workers walked an average of 12 miles per shift, with only 15% of time spent actually picking items.

Impact: Peak demand 3x capacity

Error Rates

Human picking errors resulted in returns, customer complaints, and inventory discrepancies. Error rates spiked during high-volume periods when workers were fatigued.

Impact: 3.2% mis-pick rate, costly returns

Scalability

Existing automation solutions required fixed infrastructure (conveyors, AS/RS) with lengthy installation times and couldn't adapt to changing product mix or seasonal demands.

Impact: 18-month lead time for expansion

Our Solution

We designed a complete autonomous mobile robot (AMR) system with on-device Edge AI for real-time navigation, obstacle avoidance, and fleet coordination without dependency on cloud connectivity or fixed infrastructure.

System Architecture

Decentralized swarm architecture where each robot makes autonomous decisions using TinyML while coordinating with peers through mesh networking.

Robot Perception Layer

360° LiDAR for mapping and localization
Depth cameras for obstacle detection
Floor-facing camera for QR code navigation
IMU for motion tracking and stability
Ultrasonic sensors for close-range detection

Edge AI Processing

Custom compute module with NPU accelerator
TinyML models for real-time inference
On-device SLAM (Simultaneous Localization & Mapping)
Local path planning and obstacle avoidance
Gesture and voice recognition for human interaction

Swarm Coordination

Peer-to-peer mesh networking (802.11s)
Distributed task allocation algorithm
Collision avoidance coordination protocol
Dynamic route optimization
Charging station queue management

Warehouse Management Integration

WMS API integration for order dispatch
Real-time inventory tracking
Pick station orchestration
Analytics dashboard and reporting
Predictive maintenance system

Custom Robot Hardware Design

Compute Platform	Custom board: Cortex-A78 + Mali GPU + 4 TOPS NPU
LiDAR	16-channel 3D LiDAR, 100m range, 20Hz
Cameras	4x depth cameras, 1x RGB with 120° FOV
Payload Capacity	Up to 30kg, adjustable shelving
Battery	48V LiFePO4, 8-hour runtime, hot-swap capable
Charging	Autonomous docking, 15-min fast charge to 80%
Speed	Up to 2 m/s loaded, precision positioning ±5mm

Embedded Software Stack

Real-time Linux (PREEMPT_RT) for deterministic control
ROS2-based modular architecture
On-device SLAM with loop closure
TensorFlow Lite for ML inference (<20ms latency)
Mesh networking with <50ms peer communication
Fail-safe behaviors and graceful degradation
OTA firmware updates with A/B partitioning

TinyML Navigation & Intelligence

Our robots run multiple ML models on-device for real-time decision making without cloud dependency, ensuring operation even during network outages.

Visual SLAM Enhancement

Lightweight CNN for feature extraction

Sub-centimeter localization accuracy

<10ms inference, 30 FPS

Dynamic Obstacle Classification

MobileNetV3 + custom head

98.5% object classification (humans, forklifts, pallets)

<15ms per frame

Path Prediction

LSTM for trajectory forecasting

92% human path prediction (2 sec horizon)

Pick Verification

EfficientNet-Lite for item recognition

99.7% item verification accuracy

<50ms verification time

Anomaly Detection

Autoencoder for operational anomalies

95% anomaly detection rate

Implementation Timeline

Phase 1: Facility Assessment & Design

8 weeks

Warehouse layout analysis and 3D mapping
Workflow optimization study
Robot specification and custom design
Safety assessment and compliance planning

Phase 2: Robot Development & Testing

20 weeks

Hardware prototyping and validation
TinyML model development and optimization
Fleet coordination algorithm development
Extensive simulation testing

Phase 3: Pilot Deployment

12 weeks

20-robot pilot in dedicated zone
Real-world performance optimization
Worker training and change management
Safety validation and certification

Phase 4: Full Scale Deployment

16 weeks

Production of 180+ additional robots
Full warehouse coverage deployment
WMS integration and workflow optimization
24/7 operations handover

Results & Impact

The AMR deployment transformed warehouse operations, achieving dramatic improvements in throughput, accuracy, and operational efficiency within 4 months of full deployment.

Order Throughput

Before:8,000 orders/day

After:35,200 orders/day

340% improvement

Pick Time per Order

Before:12.5 minutes average

After:2.75 minutes average

78% reduction improvement

Order Accuracy

Before:96.8% accuracy

After:99.2% accuracy

75% error reduction improvement

Labor Productivity

Before:45 picks/person/hour

After:180 picks/person/hour

300% improvement improvement

Walking Distance

Before:12 miles/shift/worker

After:0.5 miles/shift/worker

96% reduction improvement

Peak Season Scalability

Before:30% capacity increase possible

After:150% capacity increase possible

5x scalability improvement

Return on Investment

Implementation Cost

Significant capital investment in fleet

Annual Savings

62% reduction in per-order fulfillment cost

Payback Period

14 months

3-Year ROI

385%

“These robots have completely transformed our operation. We went from struggling to meet demand to having capacity headroom we never imagined. The best part is the reliability - they run 24/7 with minimal supervision. Our workers now focus on complex tasks while robots handle the repetitive walking and carrying. It's the future of warehousing.”

VP of Operations

Client Logistics Company