LSR200 Industrial 4WD ROS Research Robot Platform

AI, Perception & ROS Development Features

The LSR200 development library combines ROS navigation, perception, AI integration, interaction, outdoor autonomy, and mobile-robot control workflows. Voice-dependent functions require the corresponding voice hardware, while local AI-model workloads depend on the selected onboard computer and model requirements.

01 AI Large-Model Integration

Large Language Model Integration

Supports online LLM integration workflows for natural-language command interpretation, dialogue, and higher-level robot interaction.

Voice-Model Interaction

With compatible voice hardware and a speaker, speech-to-text and text-to-speech workflows can be combined with AI-model interaction.

Vision-Language Interaction

Camera-based workflows can analyze visual content, recognize object categories, and return text or voice responses through compatible multimodal models.

Local Model Deployment

Compatible NVIDIA Jetson configurations support local AI-model deployment for lower-latency inference and reduced dependence on cloud services.

02 AI Multimodal Applications

AI Vision: Scene Understanding & Perception

Multimodal vision workflows can analyze images or video, identify scene elements, and generate semantic feedback.

AI Recognition: Object Tracking

Vision-model workflows support target identification and real-time tracking experiments.

AI Interaction: Color-Aware Line Following

Multimodal interaction can connect color-based commands with vision line-following behaviors.

Flexible Chassis Control

AI interaction workflows can be connected to chassis motion commands, with LiDAR-based emergency-stop behavior for mobile-robot experiments.

AI Interaction: LiDAR Tracking

Natural-language interaction can be linked to LiDAR-based tracking behaviors for target-following experiments.

RAG + Dual-Model Inference Workflow

RAG workflows retrieve knowledge-base context for task interpretation, while language and multimodal models can be combined for task planning and perception-driven actions.

Voice Wake-Up & Interruption

With compatible voice hardware, new voice commands can interrupt an ongoing interaction workflow and trigger a new dialogue or task request.

03 Embodied AI SLAM Mapping & Semantic Navigation

Semantic Mapping

Multimodal interaction can be connected to mapping commands so users can initiate mapping and save maps through semantic task workflows.

Semantic Navigation

Semantic coordinate labels can be associated with map locations so navigation requests can be translated into target points.

Voice Semantic Labeling

With compatible voice hardware, semantic location labels can be added through dialogue during navigation workflows.

Combined Embodied-AI Workflow

Preconfigured interaction nodes can connect voice, perception, navigation, and mobile-robot functions for integrated embodied-AI demonstrations and secondary development.

04 High-Precision Motion Feedback

4096-Line Magnetic Encoder

The LSR200 4WD drivetrain uses high-precision 4096-line magnetic encoder feedback for wheel-speed measurement, odometry, low-speed control, and navigation experiments.

05 Outdoor Navigation & 3D LiDAR Perception

Multi-Line LiDAR Integration

The LSR200 supports 16-line and 32-line mechanical LiDAR for 360° horizontal 3D perception, outdoor point-cloud mapping, obstacle perception, and autonomous-driving research. The Ultra version includes multi-line LiDAR as standard.

Autoware Outdoor Mapping & Navigation

Autoware-oriented workflows support HD-map-based path planning for low-speed autonomous-navigation research.

Point-Cloud Obstacle Avoidance

LiDAR point-cloud clustering can be used for obstacle perception and avoidance in outdoor navigation experiments.

NVIDIA Orin controllers are suitable for higher-compute Autoware and 3D-perception workloads.

06 Autonomous Power Management (Optional)

Autonomous Docking & Recharging

Auto-recharging support enables return-to-station and docking workflows using ROS mapping and navigation. Development resources include charging-station calibration, docking methods, and low-battery return logic.

07 Ultrasonic Blind-Spot Sensing

Six-Sensor Close-Range Coverage

Four front-facing and two rear-facing ultrasonic sensors help cover close-range blind spots outside the LiDAR scanning plane, adding another sensing layer for navigation in confined environments.

08 4WD Brushless Drivetrain & Independent Suspension

576 W Four-Wheel-Drive System

Four 8-inch brushless hub motors provide 576 W combined rated power. Four-wheel independent suspension improves wheel contact, traction, and ride stability during indoor and outdoor mobile-robot experiments.

Open-Source ROS Development Library

The development library includes ROS, perception, navigation, AI, control, simulation, and secondary-development examples.

1. RTAB-Map 3D VSLAM & LiDAR Integration

Supports RTAB-Map vision mapping and LiDAR–vision fusion workflows for 3D environment reconstruction in ROS 1 and ROS 2.

2. Classic 2D LiDAR Mapping & Navigation

ROS 2 resources include Gmapping, Cartographer, and slam_toolbox. ROS 1 resources include Gmapping, Hector, Karto, and Cartographer, together with point-to-point, waypoint, and obstacle-avoidance workflows.

3. ORB-SLAM2 Visual Mapping

Uses ORB-SLAM2 for camera pose estimation and sparse 3D reconstruction, with metric-scale information available in RGB-D workflows.

4. ROS Qt Graphical User Interface

A Qt-based interface provides convenient ROS launch controls and real-time feedback such as robot velocity, battery status, and system information.

5. YOLO Object Detection

ROS 1 resources include YOLOv3 workflows, while ROS 2 resources include YOLOv8 and YOLO11 workflows for object detection and custom-model experiments.

6. LLM Deployment

Supports online LLM integration and local-model workflows on compatible onboard-compute configurations for reasoning, dialogue, and robot interaction.

7. Depth-Based Visual Following

Uses depth-camera information to estimate target distance and bearing for real-time visual-following experiments.

8. KCF Target Tracking

Includes Kernelized Correlation Filter tracking for identifying and following targets with stable visual features.

9. AR Tag Recognition & Following

Supports 6-DOF AR-tag pose detection for tag following and tag-based localization experiments.

10. RRT Autonomous Exploration

Includes an RRT-based autonomous exploration workflow for mapping, map saving, and return-to-start experiments.

11. Web-Based Camera Monitoring

Provides browser-based access to the robot camera stream for remote monitoring and development.

12. RGB Camera Line Following

Supports RGB-vision line following with LiDAR-assisted obstacle avoidance.

13. LiDAR-Based Following

Uses LiDAR scans for nearby-target selection and following experiments.

14. LiDAR Angle Masking

SDK-based LiDAR angle masking allows selected scan sectors to be filtered according to the sensor and application configuration.

15. Sound Source Localization

With compatible microphone-array hardware, sound-direction estimation supports voice-interaction and navigation experiments with 1° directional precision.

16. Voice-Activated Summoning

With compatible voice hardware, microphone-array direction information and LiDAR ranging can be combined in voice-driven summoning experiments.

17. Voice-Controlled Navigation

Voice-command workflows can associate recognized commands with predefined navigation points on a saved map.

18. TTS Interaction

Text-to-speech workflows support robot voice interaction and can be extended with compatible online voice-dialogue services.

19. TEB & DWA Path Planning

Includes tutorials and Python-based learning resources for TEB and DWA navigation and local path-planning methods.

20. DWB / MPPI / RPP Controllers

Provides DWB, MPPI, and Regulated Pure Pursuit controller examples for different Nav2 scenarios and robot footprints.

21. Full-Coverage Path Planning

Supports generation of coverage paths for user-defined areas, useful for inspection and systematic-area traversal research.

22. Chassis Kinematics Analysis

Development resources include kinematics examples for Ackermann, differential, tracked, Mecanum, omnidirectional, and 4WD chassis types.

23. Path Recording & Playback

Manual trajectories can be recorded and replayed autonomously using Nav2-based workflows.

24. URDF Models

Includes URDF models for simulation, visualization, and robot-software development based on platform geometry.

25. ROS Mobile App for Navigation

A mobile application supports robot motion control, mapping, and navigation workflows.

26. Hardware Tuning & Parameter App

Android and iOS tools support parameter tuning, gravity-sensing control, and waveform visualization for supported hardware functions.

27. Wireless Code Flashing

Bluetooth tools support wireless code flashing and remote debugging for supported controller workflows.

28. Optional 4G/5G Remote Modules

Optional cellular communication modules support long-range video transmission and remote-control development over compatible mobile networks.

29. 3D Reconstruction for Autonomous-Driving Research

With multi-line LiDAR, the platform supports outdoor 3D mapping and environment-reconstruction workflows for autonomous-driving research.

30. Autoware.Universe Multi-Point Navigation

Autoware.Universe examples include sequential waypoint dispatch using Python scripts for continuous multi-point navigation experiments.

Frequently Asked Questions

What is the LSR200 designed for?

The LSR200 is designed for ROS research, SLAM, autonomous navigation, computer vision, embodied AI, path planning, mobile-robot control, and low-speed autonomous-driving development.

What are the main chassis specifications?

The 4WD chassis supports up to 60 kg payload, reaches up to 2.1 m/s, weighs approximately 48.5 kg, measures 650 × 650 × 352 mm, climbs inclines up to 28°, and provides approximately 10.5 hours of continuous operation.

What drivetrain does the LSR200 use?

The robot uses four 8-inch brushless hub motors with 576 W combined rated power and four-wheel independent suspension.

What encoders are used?

The 4WD drivetrain uses 4096-line high-precision magnetic encoder feedback for wheel-speed measurement, odometry, and motion control.

Which onboard computers are available?

Controller options include Raspberry Pi 5 (8 GB), Jetson Orin Nano Super (8 GB), and Jetson Orin NX Super (16 GB). A Basic Package without the ROS computer, LiDAR, and depth-camera bundle is also available.

Which ROS versions are supported?

Raspberry Pi 5 supports ROS 1 Melodic and ROS 2 Humble. Jetson Orin Nano Super and Jetson Orin NX Super support ROS 1 Noetic and ROS 2 Humble.

What LiDAR options are available?

The platform supports N10P and M10P 2D TOF LiDAR as well as 16-line and 32-line mechanical LiDAR for 3D point-cloud perception. The Ultra version includes multi-line LiDAR as standard.

Does the LSR200 support RGB-D vision?

Yes. ROS configurations support depth-camera workflows for RGB-D SLAM, visual following, tracking, AR-tag detection, and other perception applications.

Does the platform include mmWave radar?

The LSR200 platform integrates MR20 mmWave radar for additional perception and sensor-fusion development alongside LiDAR, cameras, and wheel odometry.

What is the ultrasonic sensing configuration?

The 4WD platform uses six ultrasonic sensors—four at the front and two at the rear—to add close-range coverage outside the LiDAR scanning plane.

Can I use Autoware with the LSR200?

Yes. Autoware-oriented workflows include HD-map-based path planning, point-cloud obstacle avoidance, 3D reconstruction, and multi-point navigation. NVIDIA Orin controllers and multi-line LiDAR are suitable for these workloads.

What SLAM and navigation resources are included?

Development resources include RTAB-Map, Gmapping, Cartographer, slam_toolbox, ORB-SLAM2, RRT autonomous exploration, Nav2, TEB, DWA, DWB, MPPI, RPP, coverage planning, path recording and playback, and waypoint navigation examples.

Does the LSR200 support AI and large-model applications?

Yes. Development workflows include LLM integration, multimodal vision, RAG-oriented task interaction, semantic mapping and navigation, object detection, target tracking, and local-model deployment on compatible onboard-compute configurations.

Which functions require voice hardware?

Voice-driven functions such as speech interaction, voice wake-up and interruption, sound-source localization, voice-controlled navigation, voice semantic labeling, and voice-driven summoning require compatible voice hardware.

Does the robot support autonomous charging?

Yes. Autonomous charging is supported as an optional configuration, with ROS-based return-to-station, docking, charging-station calibration, and low-battery return workflows.

What development resources are included?

Development resources include source code, ROS system images, tutorials, navigation and perception examples, URDF models, controller tools, path-planning workflows, AI integration examples, mobile tools, and secondary-development resources.