NexArm ROS on its electric sliding rail beside two-tier metal shelving, with coloured sorting trays on the sandbox

Robot arms

NexArm ROS Sliding Rail Warehouse Sandbox Kit

NexArm ROS on an electric linear rail beside multi-tier shelving, for model warehouse sorting and transport.

Raspberry Pi
NVIDIA Jetson
Suggested for university

The NexArm ROS Sliding Rail Warehouse Sandbox Kit mounts the NexArm ROS robot arm on an electric linear sliding rail beside multi-tier shelving, on a metal sandbox. The rail carries the arm between stations, so it can move material between workstations, sort stock automatically and manage inbound inventory, as in an industrial warehouse. The arm brings its 3D depth camera, AI voice interaction box, multimodal large AI models and the OpenClaw agent, and a visual control hub shows two live video feeds and switches scenes with one key. It runs on a Raspberry Pi 5 or NVIDIA Jetson with an ESP32 controller, and Hiwonder supplies tutorials, source code and system images.

Zeta42 is an authorised Hiwonder partner in the Middle East.

Configurations

Choose how it ships.

Model: Without Raspberry Pi Controller · With Raspberry Pi 5 (4GB) · With Raspberry Pi 5 (8GB) · With Raspberry Pi 5 (16GB) · Without Jetson Controller · With Jetson Nano(4GB) · With Jetson Orin Nano Super(4GB) · With Jetson Orin Nano Super(8GB) · With Jetson Orin NX Super(8GB) · With Jetson Orin NX Super(16GB)

What it does

Built for.

Electric linear rail
The rail gives up to 478 mm of travel, which widens the arm's reach across the desk and lets it serve several workstations. Transport speed and direction are adjustable.
Warehouse workflows
Multi-tier shelving, sorting trays and a recognition zone support multi-station material handling, automated sorting and inbound inventory control.
Visual control hub
Two real-time video feeds, one-key scene switching and custom hardware settings make transport and sorting easy to watch and control.
3D vision and hand-eye coordination
Fusing colour and depth lets the arm see colours and point clouds, and inverse kinematics turns that into 3D grasping, sorting and transporting.
Voice, large AI models and OpenClaw
The AI voice box takes spoken requests, and speech, language and vision models working with the OpenClaw agent handle tasks such as understanding a scene, tracking a moving object and gathering data into reports.
Choosing a controller
Choose a Raspberry Pi 5 or a Jetson Nano, Orin Nano Super or Orin NX Super. Hiwonder recommends an Orin controller for deeper development of AI models. Kits without a controller come with the matching expansion board and enclosure, but no microSD card or SSD.

Where it is used

Applications.

Teaching smart warehousing and logistics workflows
Multi-station transport and automated sorting exercises
University ROS 2 and embodied AI labs
Inventory and inbound control projects with machine vision

Videos

See it run.

Specifications

The full sheet.

Weight: 8.8kg
Sandbox dimensions: 608 × 608 mm
Electric sliding rail: 478 mm effective stroke; 601 × 189 × 65 mm; 10 kg maximum payload
Material: Metal brackets
ROS controller: Raspberry Pi 5 / Jetson Nano / Jetson Orin Nano / Jetson Orin NX
ESP32 controller: ESP-WROOM main chip
Camera: Depth camera
Servo models: HX-12H / HX-30HM / HX-65HM intelligent bus servos
Control methods: ROS topics/nodes, Python programming, app control, PC software control
Power supply: 12V 5A DC power adapter
Operating system: Ubuntu 22.04 + ROS 2 Humble
Software: iOS/Android app, Windows PC software
Communication: USB, Wi-Fi, Ethernet
Programming tools: Python, C, C++
Storage: 64 GB TF card (Raspberry Pi 5 / Jetson Nano); 128 GB SSD (Orin Nano / Orin NX)
Included materials: Tutorials, ROS source code, ESP32 source code, system images, supporting software