Autonomous mobile robot powered by NVIDIA Jetson edge computing — process AI inference on-board with sub-50ms latency, navigate via SLAM + LiDAR + vision fusion, and integrate seamlessly with your WMS for intelligent warehouse automation.
An Edge AI AMR is an autonomous mobile robot equipped with an on-board GPU computing module (NVIDIA Jetson Orin or Intel Core i7 + VPU). Instead of sending sensor data to a remote cloud server for processing, the robot runs AI inference locally — performing object detection, semantic segmentation, SLAM localization, and path planning in real time on its own hardware.
Cloud-based AMR architectures introduce 100–500 ms round-trip latency for every decision. Edge AI AMR robots process sensor data and execute navigation commands within 10–50 ms, enabling predictive obstacle avoidance, dynamic route re-planning, and continuous operation even when network connectivity is intermittent or lost entirely.
The edge computing module runs optimized neural networks via TensorRT and cuDNN: real-time object detection (YOLO-family models), semantic segmentation for scene understanding, behavior prediction for dynamic environments, and visual SLAM for precise localization — all simultaneously at up to 275 TOPS of INT8 throughput.
On-board YOLO-family neural networks detect pallets, humans, forklifts, and obstacles at 30+ FPS. Semantic segmentation distinguishes object classes for intelligent, context-aware navigation decisions without cloud dependency.
Edge-computed route re-planning executes in under 50 ms. The robot continuously evaluates the optimal path using real-time sensor fusion, adapting to moving obstacles, aisle congestion, and changing warehouse layouts on the fly.
From sensor input to actuator command, the full inference pipeline completes within 50 ms. This ultra-low latency enables predictive collision avoidance and smooth trajectory control critical for safe human-robot coexistence.
When Wi-Fi or 5G connectivity drops, the robot continues full autonomous navigation using on-board SLAM and path planning. Task queues are cached locally and synchronized once the connection resumes — zero downtime.
Deploy up to 500+ edge AI AMRs in a single facility. The centralized fleet manager handles intersection reservations, congestion avoidance, battery-aware task scheduling, and load-balanced order assignment across the entire fleet.
360° LiDAR + 3D ToF cameras + ultrasonic sensors provide triple-redundant obstacle detection. ISO 3691-4 compliant safety PLC, front/rear safety scanners, and emergency stop circuits ensure safe operation around personnel.
The robot autonomously drives through the facility to build a high-resolution semantic map using LiDAR SLAM. No reflectors, magnets, or floor markers required. Map generation for a 10,000 m² warehouse completes in under 2 hours.
Connect the fleet manager to your existing WMS, MES, or ERP via REST APIs, MQTT topics, or VDA 5050 protocol. The integration layer handles task assignment, inventory synchronization, location reservation, and real-time status reporting.
Define traffic rules, speed zones, charging station locations, and task priorities in the fleet management console. The system auto-optimizes intersection reservations and congestion avoidance across all robots.
Start with a single robot for validation, then scale to 500+ units. OTA firmware updates and remote model retraining allow continuous improvement without on-site engineering visits. 24/7 remote monitoring dashboard included.
The robot is equipped with an NVIDIA Jetson AGX Orin module delivering up to 275 TOPS of INT8 AI performance, or an Intel Core i7 + VPU combination for lighter workloads. This on-board compute runs object detection, semantic segmentation, SLAM, and path planning simultaneously — all processed locally without cloud dependency.
The robot uses multi-sensor SLAM fusion combining 2D/3D LiDAR, visual odometry from stereo cameras, and IMU data. It builds a semantic map of the facility during an initial commissioning run and re-localizes within ±10 mm accuracy. No reflectors, magnetic tape, or QR codes are needed.
The edge AI AMR continues full autonomous navigation using on-board SLAM and cached path plans. Active tasks are stored locally and automatically synchronize with the fleet manager when connectivity is restored. The robot never stops working due to network interruptions.
The fleet management platform exposes REST APIs, MQTT message topics, OPC-UA endpoints, and supports the VDA 5050 standard for AGV communication. Your WMS can send pick orders, receive completion confirmations, and monitor real-time robot status through standardized interfaces.
Yes. The robot is available in configurations rated for -25 °C to +45 °C with heated sensor housings for cold-chain applications. An IP65-rated variant protects against dust and water ingress for manufacturing environments. The edge computing module is industrial-grade with extended temperature tolerance.
Get expert consultation on edge AI AMR robot selection, WMS integration, and deployment planning. Our team supports warehouse automation projects from feasibility study to full-scale deployment.