ON THIS PAGE
AMR Controller Solutions
Reliable Embedded Computing Solutions for Autonomous Service Robots & AMRs
The transition from robot prototype to mass deployment is where most projects fail. When CV, SLAM, motion control, and connectivity run on separate boards with different BSPs, version synchronization becomes impossible, debugging becomes a nightmare, and scaling costs explode. Our integrated domain controller architecture provides the foundation for reliable, scalable autonomous robot deployments.
Faster Integration
Reduce development cycles through unified architecture that consolidates CV, SLAM, motion control, and connectivity into a single platform. Pre-validated BSP and reference designs accelerate time-to-market by 40-60%.
Reliable Deployment
Field-proven reliability with 99.5%+ uptime across 10,000+ deployed units. Industrial-grade components, comprehensive diagnostics, and robust OTA infrastructure ensure stable operation in demanding environments.
Scalable Production
Architecture designed for mass production replication. Unified firmware images, locked BOM, and 5-7 year supply chain commitment eliminate configuration drift and ensure consistent quality at scale.
Quick Input Checklist:
Industry Reality: Why Robot Pilots Fail to Scale
The service robot industry is entering a critical maturation phase. Early adopters have deployed pilot programs in hotels, malls, hospitals, and industrial facilities—but the transition from successful pilots to profitable fleet operations remains the primary barrier to market expansion.
A 5-robot pilot that works reliably in a controlled environment often collapses at 50 units. Configuration drift between robots causes inconsistent behavior. Firmware version mismatches create debugging nightmares. Lack of unified diagnostics means on-site technician visits for issues that should be resolvable remotely. The O&M cost per robot scales linearly—or worse—destroying unit economics.
Companies that invest in robust controller architecture now gain a compounding advantage: faster development cycles, lower deployment risk, and sustainable O&M costs at scale. The controller platform decision determines whether your robot business can achieve profitability.
Key Challenges in Robot Controller Architecture
Architecture Fragmentation
When CV, SLAM, motion control, and UI run on separate boards with different BSPs, version synchronization becomes nearly impossible. Configuration drift between units causes inconsistent behavior, making debugging a nightmare and scaling prohibitively expensive.
Integration Complexity
Multi-board designs require complex inter-board communication, leading to latency issues (50ms+ typical), synchronization failures, and cascading bugs that are difficult to trace. Each integration point becomes a potential failure mode.
Field Reliability Gaps
Consumer-grade components fail in industrial environments. Thermal issues, EMI interference, and power instability cause intermittent failures that only appear after deployment. Recovery from these failures often requires on-site intervention, driving up O&M costs.
What this means for robot makers: Controller architecture decisions made during prototype development will determine your ability to scale. Choosing a fragmented multi-board approach may seem flexible initially, but creates compounding technical debt that becomes prohibitively expensive to address after production begins.
Solution Overview: Integrated Domain Controller Architecture
Our AMR controller solution consolidates CV perception, SLAM navigation, motion control, UI interaction, and fleet connectivity into a single industrial-grade platform with unified software architecture. This approach—proven in automotive ADAS—provides the foundation for reliable, scalable robot deployments.
Edge AI Processing
Up to 6 TOPS NPU for on-device inference—speech, vision, and custom AI models without cloud dependency.
SLAM Navigation
Multi-sensor fusion for reliable localization, dynamic obstacle avoidance, and multi-floor operation.
Motion Control
Deterministic CAN bus communication with <5ms latency for precise chassis and motor control.
Fleet Management
Remote diagnostics, OTA updates, and centralized logging for efficient large-scale fleet operations.
System Architecture
┌─────────────────────────────────────────────────────────────┐ │ DOMAIN CONTROLLER │ ├─────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ SLAM │ │ CV │ │ Motion │ │ Fleet │ │ │ │Navigation│ │Perception│ │ Control │ │ Mgmt │ │ │ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ │ │ │ │ │ └─────────────┴─────────────┴─────────────┘ │ │ │ │ │ Unified Memory / Low-Latency Bus │ │ │ │ ├─────────────────────────┴───────────────────────────────────┤ │ SENSOR LAYER │ │ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ │ │ │ LiDAR │ │ Camera │ │ IMU │ │Ultrasonic│ │ │ └────────┘ └────────┘ └────────┘ └────────┘ │ ├─────────────────────────────────────────────────────────────┤ │ ACTUATOR LAYER │ │ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ │ │ │ Motor │ │ Arm │ │Display │ │ Safety │ │ │ │ Driver │ │ Control│ │ / UI │ │ I/O │ │ │ └────────┘ └────────┘ └────────┘ └────────┘ │ └─────────────────────────────────────────────────────────────┘
The integrated domain controller architecture consolidates all robot intelligence on a single SoC with shared memory and low-latency internal buses. This eliminates inter-board communication overhead, reduces latency from 50ms+ to under 5ms, and enables atomic firmware updates that guarantee consistent behavior across the fleet.
Sensor inputs from LiDAR, cameras, IMU, and ultrasonic sensors are processed through unified drivers. The NPU accelerates CV and SLAM algorithms while the CPU handles navigation planning and fleet communication. Motion commands are issued through deterministic CAN bus interfaces with hardware-guaranteed timing.
Integration Interfaces
Sensor Interfaces
USB 3.0, MIPI CSI, Ethernet, RS-485
Motion I/O
CAN Bus (2.0B), GPIO, PWM
Connectivity
5G/LTE, Wi-Fi 6, BT 5.0
Core Modules
Each module addresses a critical capability for autonomous robot operation. The integrated architecture ensures these modules work together seamlessly, with shared memory, unified logging, and coordinated resource management.
Perception & SLAM Navigation
Reliable autonomous navigation relies on tight integration between multi-sensor fusion and the underlying middleware. To accelerate your software development, our perception module provides native Board Support Packages (BSP) for ROS 1 (Noetic) and ROS 2 (Humble), optimized with hardware-accelerated DDS communication to minimize inter-node latency.
The controller handles real-time sensor fusion across LiDAR, RGB-D cameras, 6-axis IMU, and wheel odometry. By offloading visual feature extraction for V-SLAM algorithms to the integrated NPU, we free up critical CPU cycles for complex path planning and dynamic obstacle avoidance.
This robust architecture prevents map drift in dynamic environments (moved furniture, crowded spaces). When localization confidence drops, the system seamlessly initiates recovery-to-known-position protocols rather than continuing with degraded accuracy—a methodology validated across 10,000+ field deployments.
Motion Control & Safety Logic
Deterministic motion control separates reliable robots from prototypes. Our motion control module features dedicated CAN Bus (2.0B) interfaces with native CANopen protocol support, delivering strictly deterministic <5ms command latency for smooth trajectory execution and responsive obstacle avoidance.
Crucially for industrial deployments, all motion and serial interfaces feature 2.5KV galvanic and optocoupler isolation. This hardware-level protection completely shields the mainboard logic from destructive back-electromotive force (back EMF) and voltage spikes generated by heavy chassis motors during sudden braking or emergency stops.
Safety-critical sensors (ultrasonic, bumpers, cliff detectors) bypass the OS task scheduler entirely. They are routed through dedicated safety I/O channels with hardware interrupt priority, ensuring that safety-rated deceleration curves and emergency stop logic execute deterministically regardless of the CPU load.
AI Capability & Edge Inference
On-device AI processing eliminates cloud dependency, reduces latency, and protects privacy. Powered by the industrial-grade RK3588/RK3588J SoC, our platform utilizes a true heterogeneous computing architecture—combining an Octa-core CPU, Mali-G610 GPU, and a 6 TOPS NPU—to optimize performance-per-watt for battery-powered AMRs.
To alleviate deployment anxiety for algorithm engineers, we provide comprehensive toolchain support. The native RKNN Toolkit enables seamless conversion, quantization, and deployment of models from TensorFlow, PyTorch, ONNX, and Caffe. For models containing unsupported operators, our compiler automatically implements fallback routing to the CPU or GPU, ensuring your custom vision or speech models run efficiently out of the box.
The AI stack is designed for field updates. New inference models can be deployed via OTA without touching the underlying firmware, enabling continuous improvement of recognition accuracy based on collected operational data.
Connectivity & Fleet Management
Service robots must operate reliably in environments with intermittent connectivity—elevator shafts, basement parking, and network congestion during peak hours. Our connectivity module implements offline-first architecture where robots maintain full autonomous capability without server dependency, using network only for coordination and diagnostics when available.
The OTA update infrastructure uses A/B partition schemes for atomic rollback, differential updates to minimize bandwidth, and staged rollouts with automatic health verification. This battle-tested approach has delivered 10,000+ successful fleet updates without bricking incidents.
Remote diagnostics provide structured logging, system health metrics, and secure shell access for engineering troubleshooting. Integration APIs for fleet management platforms enable centralized monitoring, task dispatch, and maintenance scheduling across geographically distributed robot deployments.
Application Scenarios
Our AMR controller architecture supports diverse robot applications, from commercial service robots to industrial inspection systems. Each scenario presents unique navigation, perception, and reliability requirements that inform controller configuration and BSP customization.
Commercial Service Robots
Hotel Delivery / Mall Guidance / Public Service Halls
Commercial service robots operate in the most demanding human-centric environments: hotel lobbies with constant guest traffic, shopping malls with unpredictable crowds, and government service halls where reliability directly impacts public perception. These robots must navigate dynamic spaces where furniture moves daily, seasonal decorations change layouts, and peak-hour congestion creates navigation challenges that laboratory testing never reveals.
The deployment pain points are substantial. Elevator integration requires coordination with building management systems that use proprietary protocols. Weak WiFi in elevator shafts and underground parking areas causes task queue failures. Guest interactions demand natural language understanding and appropriate responses that reflect the brand’s service standards.
A system-level architecture addresses these challenges by unifying navigation, perception, and interaction on a single platform. When the robot encounters an occupied elevator, the controller coordinates retry logic, alternative routing, and task queue reordering—all within milliseconds.
Best for: Hotel chains, property management companies, shopping mall operators, government service digitization integrators
Inspection & Patrol Robots
Campus Security / Power Substations / Data Centers
Inspection and patrol robots operate in environments where human presence is either dangerous, costly, or insufficient for continuous monitoring. Power substations require thermal imaging to detect equipment failures before outages occur. Data centers need 24/7 monitoring of server racks for temperature anomalies and physical security breaches.
These applications impose the most stringent reliability requirements in service robotics. A security patrol robot that fails at 2 AM cannot call for human backup—it must either self-recover or gracefully degrade to a known-safe state while alerting the operations center. An inspection robot in a power substation operates in high-EMI environments where poorly designed electronics will malfunction or provide false readings.
The perception requirements are specialized: thermal cameras for electrical equipment inspection, high-sensitivity cameras for low-light patrol, and multi-spectral sensors for specific anomaly detection.
Best for: Power grid operators, data center operators, industrial facility managers, security service companies
Home & Personal Service Robots
Elderly Care / Home Security / Companion Interaction
Home and personal service robots represent the next frontier of consumer robotics, moving beyond vacuum cleaners to provide meaningful assistance, security monitoring, and social interaction. These robots must operate in unstructured home environments where every household has unique layouts, furniture arrangements, and usage patterns.
The core technical challenges center on adaptability and trust. The robot must build and maintain maps of spaces that change frequently—moved chairs, new furniture, seasonal items. It must recognize family members, distinguish guests from intruders, and respond appropriately to voice commands in acoustically challenging environments.
Consumer price pressure demands cost-optimized hardware, but performance expectations remain high. The solution lies in integrated architecture that maximizes the capability of each silicon dollar.
Best for: Consumer electronics brands, elderly care service providers, smart home integrators, companion robot startups
Autonomous Cleaning Robots
Shopping Malls / Hotels / Airports / Industrial Facilities
Commercial cleaning robots must deliver consistent cleaning quality across large floor areas while operating around business hours, avoiding obstacles, and minimizing disruption to human activities. Unlike consumer robot vacuums, commercial units cover tens of thousands of square meters per shift, manage water and detergent supplies, and must integrate with building schedules.
The operational reality is harsh. Cleaning chemicals create corrosive environments for electronics. Water ingress risks are constant. The robot navigates freshly cleaned (wet) floors with different traction characteristics than dry surfaces. Trash, spilled liquids, and unexpected obstacles require real-time replanning.
Efficiency optimization requires understanding cleaning patterns: which areas need frequent attention, which can be cleaned weekly, how to minimize water consumption while maintaining quality standards.
Best for: Facility management companies, commercial cleaning service providers, airport operators, shopping mall property managers
Business Value
For robot makers and system integrators evaluating controller platforms, the architecture decision directly impacts development velocity, deployment risk, and long-term O&M economics. Here’s how our integrated approach delivers measurable business value:
40-60% Faster Time-to-Market
Unified architecture eliminates multi-board integration complexity, pre-validated BSP reduces development cycles.
99.5%+ Field Uptime
Industrial-grade reliability with comprehensive diagnostics and robust OTA infrastructure ensures stable deployment.
40-60% Lower O&M Cost
Remote diagnostics eliminate 80% of on-site visits, standardized hardware reduces spare parts inventory.
5-7 Year Supply Assurance
Locked BOM and long-term component availability commitment eliminates redesign risk during product lifecycle.
Seamless Scale-Up
Single firmware image, unified logging, and deterministic behavior ensure consistent quality at mass production scale.
Delivery Path: From Evaluation to Mass Production
Requirement Review
Joint review of robot type, navigation requirements, sensor configurations, and fleet management needs.
Architecture Proposal
Tailored controller selection specifying hardware platform, BSP customization, and integration interfaces.
BSP & Integration
Board Support Package development, sensor driver integration, and ROS/SLAM algorithm porting.
Prototype Validation
Functional validation on prototype hardware, navigation testing, and AI model deployment verification.
Pilot Deployment
Field pilot with 5-10 units, real-world navigation validation, and fleet management system integration.
Mass Production
Production line setup, factory flashing tools, quality control, and ongoing technical support.
Frequently Asked Questions
Why do many robot pilots fail to scale to mass production?
Most pilot failures stem from architecture fragmentation. When CV, SLAM, motion control, and UI run on separate boards with different BSPs, version synchronization becomes nearly impossible. A successful pilot with 5 robots often collapses at 50 units due to configuration drift, firmware mismatches, and lack of unified diagnostics. Integrated domain controller architecture solves this by providing a single firmware image, unified logging, and deterministic boot sequences that replicate identically across thousands of units.
What is an integrated domain controller for service robots?
An integrated domain controller consolidates CV perception, SLAM navigation, motion control, UI interaction, and remote connectivity into a single hardware platform with unified software architecture. Unlike distributed designs where each function runs on separate boards, the domain controller shares memory, reduces inter-board latency from 50ms+ to under 5ms, and enables atomic OTA updates. This architecture is standard in automotive ADAS and is now essential for scalable service robotics.
ARM vs x86: how to choose for service robot main controller?
ARM (RK3588-based) is optimal for 90% of service robots: lower power (15W vs 45W), fanless operation, native NPU for edge AI, and better cost structure at scale. x86 is preferred when you need legacy Windows software compatibility, specific PCIe peripherals, or compute-intensive workloads beyond 20 TOPS. For delivery robots, guidance robots, and cleaning robots, ARM provides the best power-performance-cost balance.
What are common causes of SLAM navigation failure in deployment?
Field SLAM failures typically originate from: (1) dynamic environment changes (moved furniture, seasonal decorations) causing map drift, (2) reflective surfaces (glass, mirrors) creating phantom obstacles, (3) featureless corridors where visual/LiDAR features are insufficient, (4) multi-floor elevator transitions losing localization, and (5) sensor degradation over time. Robust deployments require map update mechanisms, sensor fusion redundancy, and recovery-to-known-position protocols.
How to handle weak network and offline mode for service robots?
Service robots must assume network is unreliable. Design for offline-first operation: cache task queues locally, run AI inference on-device (not cloud), store 72+ hours of logs for delayed sync, and implement graceful degradation when connectivity drops. The controller should maintain full autonomous capability without server dependency, using network only for fleet coordination, map updates, and remote diagnostics when available.
How to integrate elevators and access control systems?
Elevator integration requires protocol adaptation (typically via RS485/CAN to the elevator controller), timing synchronization for door open/close sequences, and fallback logic when elevator is occupied or offline. Access control integration (turnstiles, automatic doors) follows similar patterns. The robot controller must handle timeout conditions, retry logic, and alternative routing. We provide reference implementations for major elevator brands and building management systems.
How to ensure OTA upgrade stability for deployed robot fleets?
Stable OTA requires: (1) A/B partition scheme for atomic rollback, (2) differential updates to minimize bandwidth, (3) staged rollout (1% → 10% → 100%) with automatic rollback triggers, (4) pre-flight hardware compatibility checks, (5) post-update health verification before confirming success. Never push updates to all robots simultaneously. Our BSP includes battle-tested OTA infrastructure used across 10,000+ deployed units.
How to do remote maintenance and fault diagnosis?
Effective remote diagnostics require structured logging (not just printf), system health metrics (CPU/memory/temperature/network), event correlation across subsystems, and secure remote shell access for engineering. The controller should expose diagnostic APIs for fleet management platforms. We provide log aggregation schemas, Grafana dashboard templates, and remote debugging tunnels that work through NAT and firewalls.
Do you support ROS and third-party SLAM algorithms?
Yes. Our platform supports ROS1 (Melodic/Noetic) and ROS2 (Foxy/Humble) with optimized DDS configurations. For SLAM, we provide integration guides for Cartographer, RTAB-Map, ORB-SLAM3, and commercial solutions like Slamtec and INDEMIND. The NPU can accelerate visual SLAM feature extraction. We recommend starting with our reference SLAM stack, then migrating to your preferred algorithm once basic integration is validated.
What if AI model deployment faces missing operators on NPU?
NPU operator coverage is never 100%. When a model contains unsupported operators, you have three options: (1) modify the model to use supported equivalents, (2) run unsupported layers on CPU/GPU with automatic heterogeneous scheduling, (3) request custom operator implementation from our engineering team. We provide model analysis tools that identify compatibility issues before deployment and suggest optimization paths.
How to reduce O&M cost for large robot fleets?
O&M cost reduction comes from: (1) proactive fault detection before failures occur, (2) remote diagnosis eliminating 80% of on-site visits, (3) standardized hardware reducing spare parts inventory, (4) unified firmware simplifying training, (5) automated recovery reducing manual intervention. Our architecture typically reduces per-robot O&M cost by 40-60% compared to distributed designs through these mechanisms.
How to manage version control and lifecycle for mass production?
Mass production requires: (1) immutable firmware images with cryptographic signing, (2) hardware revision tracking linked to compatible firmware versions, (3) production line flashing tools with verification, (4) field upgrade policies with approval workflows, (5) end-of-life sunset planning. We provide complete lifecycle management tooling including factory provisioning, fleet version dashboards, and compliance audit trails.
Start Your AMR Controller Evaluation
Whether you’re developing a new robot platform or migrating from a fragmented architecture, our engineering team can provide technical guidance and platform recommendations tailored to your specific requirements.