LSR280 Four-Wheel Independent Steering ROS Research Robot Platform

LSR280 Four-Wheel Steering Robot: Key Features

The LSR280 is a four-wheel independently driven and steered ROS mobile robot platform for robotics research, autonomous navigation, mobile perception and embodied AI development. It supports forward and reverse movement, dual-Ackermann steering, crab motion and zero-radius rotation. Advanced perception, voice interaction and outdoor navigation workflows depend on the selected sensors, onboard computer and software configuration.

AI Perception and Interaction

Multimodal AI and Visual Perception

With a compatible depth camera, AI computing platform and application software, LSR280 can be used for object detection, person tracking, visual recognition and scene-understanding experiments. Voice-based tasks require the M260C audio module and appropriate speech software.

AI Model Integration: Language, Voice and Vision

The platform supports research workflows that combine language-model reasoning with visual input and speech interaction. Language services may run online; local inference depends on the installed Jetson computer and the specific model requirements. Voice input and output require the compatible audio module.

Four-Wheel Mobility for AI Tasks

Four independent drive and steering assemblies enable forward and reverse travel, coordinated front-and-rear Ackermann steering, diagonal crab motion and rotation around the chassis center. These motion modes can be used for navigation and movement planning in confined environments.

Omnidirectional Maneuvering and Motion Commands

The independent steering geometry supports lateral and diagonal repositioning without the side-slipping motion characteristic of skid-steering vehicles. The illustrated command sequences demonstrate turning, reversing and obstacle-responsive travel in configured robot control workflows.

Drive System and Mechanical Design

Low-Slip Steering Motion

Coordinated wheel steering reduces the need to drag tires sideways when changing direction. This supports smoother motion and can help reduce lateral tire slip compared with fixed-wheel skid steering under comparable operating conditions.

Sensor Integration and Development Interfaces

The illustrated platform provides integration pathways for sensors and modules using CAN, UART and Wi-Fi. Compatible LiDAR, vision and audio devices can be used with higher-level robotics software, subject to the actual controller and interface configuration.

Efficient Coordinated Four-Wheel Motion

Independent steering and coordinated wheel-speed control support smooth trajectory tracking and direction changes. This is relevant to mobile robot control research, indoor navigation and embodied AI movement tasks.

Brushless Hub Drive Motors

The LSR280 uses four 120 W drive motors, for a combined rated drive power of 480 W. Encoder-based feedback supports speed and odometry control. The chassis specification gives a maximum travel speed of 2.0 m/s.

Optional Millimeter-Wave Radar

The MR20 77 GHz millimeter-wave radar is fitted to the Plus and Super configurations. It uses FMCW ranging for short-range obstacle and moving-object sensing, with a stated detection range of 0.10–20 m, distance measurement accuracy of ±0.1 m and a 40 ms update period. The radar is an additional sensing input, not a replacement for the navigation LiDAR.

Outdoor 3D Mapping and Autoware

Outdoor 3D mapping and Autoware-oriented experiments use a higher-performance computer and a compatible multi-line LiDAR. The illustrated workflows include mapping examples, sequential navigation goals and LiDAR-based or fused obstacle perception. The specified configuration for these workflows is Jetson Orin NX 16GB with 32-channel 3D LiDAR; they are not standard functions of every LSR280 version.

32-Channel 3D LiDAR for Outdoor Perception

The Super configuration uses the WLR-722 32-channel 3D LiDAR for point-cloud acquisition and outdoor mapping research. It supports 360° horizontal scanning and a stated measurement distance of up to 150 m. Vertical field-of-view figures differ between supplied product images, so this value is omitted here.

Additional Platform Capability Illustration

The platform may be equipped with application-specific sensors and navigation software for robotics research. The exact set of supported functions depends on the Standard, Plus or Super configuration and installed development packages.

ROS Development Examples and Learning Resources

The following images illustrate robotics development topics from a broader ROS learning library. They are reference examples, not a confirmed list of software packages delivered with the LSR280. Availability, supported ROS distribution, required sensors and platform-specific adaptations depend on the purchased software and controller configuration.

RTAB-Map 3D Mapping

RTAB-Map is used in RGB-D and LiDAR–vision mapping workflows for three-dimensional environment reconstruction.

2D LiDAR SLAM and Navigation

Examples illustrate 2D mapping and localization approaches such as Gmapping, Cartographer and slam_toolbox, plus waypoint navigation.

ORB-SLAM2 Visual Mapping

Visual SLAM examples demonstrate camera pose tracking and sparse map reconstruction.

ROS Qt Monitoring Interface

A graphical interface can present ROS launch controls, motion feedback and system status.

YOLO Object Detection

Visual perception examples illustrate object recognition with YOLO-family detection models.

Large Language Model Workflows

Examples cover online language services and resource-dependent local AI inference.

RGB-D Visual Following

Depth images can help estimate target distance and bearing for visual following.

KCF Object Tracking

Kernelized Correlation Filter tracking is illustrated for camera-based target tracking.

AR Marker Recognition

Fiducial marker pose estimation can be used for localization and target-following exercises.

RRT Autonomous Exploration

Rapidly Exploring Random Tree techniques are illustrated for exploration and mapping experiments.

Browser-Based Camera Monitoring

A browser interface can be used to monitor streamed camera imagery.

Vision-Based Line Following

An RGB camera can support line-tracking exercises in a configured robot application.

LiDAR Target Following

Range measurements can be processed for nearby-target selection and tracking.

LiDAR Scan-Angle Filtering

Angular scan masks can suppress selected LiDAR sectors in perception workflows.

Sound Source Localization

A microphone array can provide direction estimates for audio interaction experiments.

Voice Summoning

Speech and audio-direction signals can be combined with navigation software for summoning demonstrations.

Voice Destination Commands

Recognized speech commands can be associated with named map locations.

Text-to-Speech Interaction

Text-to-speech modules can provide spoken robot feedback.

TEB and DWA Local Planning

Classical local-planning tutorials illustrate TEB and Dynamic Window Approach methods.

Nav2 Controller Comparisons

Examples explore DWB, MPPI and Regulated Pure Pursuit controller approaches.

Coverage Path Planning

Coverage algorithms generate routes through a defined area for inspection-like tasks.

Mobile Robot Kinematics

Learning materials compare motion constraints of different wheel and track configurations.

Route Recording and Replay

Trajectory capture and waypoint replay are illustrated in navigation examples.

URDF Models and Visualization

URDF is used to describe robot geometry and kinematic structure for software visualization.

Mobile Navigation Application

Mobile-app examples illustrate teleoperation, map viewing and navigation controls.

Hardware Parameter Tuning

Mobile diagnostic interfaces may display hardware parameters and real-time signals.

Wireless Controller Programming

Examples illustrate wireless programming and debugging of compatible controllers.

Optional Cellular Communications

Cellular links may be used for remote monitoring and control with compatible external modules.

Outdoor 3D Reconstruction

Multi-line LiDAR enables point-cloud mapping examples for outdoor robotics research.

Autoware Waypoint Navigation

Autoware examples illustrate multi-point goal dispatch and navigation visualization.

Frequently Asked Questions

What is the LSR280 designed for?

The LSR280 is a four-wheel-independent-drive-and-steering mobile robot platform for ROS-based research, autonomous navigation, embodied AI, robotics education and system integration.

What is the difference between Standard, Plus and Super?

All three configurations share the same four-wheel-steering chassis and multi-mode joystick. Standard provides the base platform without the listed camera, LiDAR, voice module or touchscreen. Plus adds a binocular depth camera, M10P 2D LiDAR, M260C smart speaker, touchscreen and millimeter-wave radar. Super includes these features but uses a WLR-722 32-channel 3D LiDAR instead of the M10P and adds automatic recharging.

Which motion modes does the LSR280 support?

The LSR280 supports forward and reverse travel, dual-Ackermann steering, crab or lateral movement and on-the-spot rotation. Each wheel has independent drive and steering actuation.

What are the payload, dimensions and platform weight?

The maximum payload is 70 kg. The platform measures 640 × 542 × 389.1 mm and has a chassis weight of 50 kg.

What is the maximum travel speed?

The maximum specified travel speed is 2.0 m/s.

What drive and steering motors are used?

The LSR280 uses four 120 W hub servo drive motors and four independent brushless steering servo motors. Absolute encoders provide steering-position feedback.

What battery does the platform use?

The platform uses a 25.55 V, 20 Ah lithium iron phosphate (LiFePO4) battery. Typical operating endurance is approximately 10 hours under reference conditions; actual runtime depends on payload, speed, terrain, computing hardware and installed sensors.

Does the LSR280 support ROS and ROS 2?

Yes. The LSR280 supports ROS and ROS 2 development workflows and provides SDK integration capabilities. The software environment and available packages depend on the selected onboard computer and configuration.

Which onboard computers are available?

Controller options include NVIDIA Jetson Orin Nano Super 8GB and Jetson Orin NX Super 16GB configurations. The Orin Nano Super option is listed at 67 TOPS, while the Orin NX Super option is listed at 157 TOPS. Both are offered with 256GB SSD storage in the controller configuration shown.

Which LiDAR sensors are included in each version?

Standard does not include LiDAR. Plus includes the M10P 2D LiDAR, while Super includes the WLR-722 32-channel 3D LiDAR. The WLR-722 supports up to 150 m measurement distance, and the M10P provides 2D scanning at 12 Hz.

Can the LSR280 perform SLAM and autonomous navigation?

Yes. With an appropriate controller, sensors and software configuration, the LSR280 can be used for mapping, localization, route planning and autonomous navigation development using ROS-based workflows.

Can the LSR280 run Autoware?

Autoware-oriented outdoor 3D mapping and navigation workflows are available with a suitable high-performance configuration. The described setup uses the Jetson Orin NX 16GB controller with a 32-channel 3D LiDAR; the Orin Nano 8GB configuration is intended for less demanding workflows.

Does it support automatic recharging?

Automatic recharging is included in the Super configuration. The robot can navigate toward a designated charging station and dock for charging as part of a configured navigation and docking workflow.

Is voice interaction supported?

Plus and Super include the M260C smart speaker module. With suitable voice-recognition and application software, it can be used for voice commands, interaction and voice-assisted navigation development. Standard does not include the voice module.

Can I develop AI vision and language-model applications?

Yes. With compatible cameras, onboard computing and application software, the LSR280 can support vision perception, object recognition and multimodal AI research. Local model performance depends on the selected computer, model size and software requirements.

Can the upper cover be removed for customization?

Yes. The removable upper cover supports installation, wiring changes and integration of additional research hardware and sensors.