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LSV550 Educational ROS Robot Platform | 4WD, Mecanum & Ackermann
- 100% Open-Source Code
- Professional Technical Support
- Comprehensive Development Resources
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The LSV550 is a configurable educational ROS robot platform for mobile-robot control, SLAM, autonomous navigation, computer vision, sensor fusion, AI interaction, and university robotics development. It is available with 4WD, Mecanum-wheel, and Ackermann chassis options, together with Standard, Pro, and Ultra sensor packages and multiple Raspberry Pi / NVIDIA Jetson controller choices.
- Three chassis options: 4WD, Mecanum Wheel, and Ackermann
- MG513 metal gear reduction motors with 500-line AB-phase GMR encoder feedback
- STM32F407VET6 low-level controller with app, smart-controller, CAN, and serial control methods
- Raspberry Pi 5, Jetson Nano, Jetson Orin Nano Super, and Jetson Orin NX Super controller options
- ROS 1 / ROS 2 system images plus FreeRTOS support for STM32 development
- N10P / M10P 360° TOF LiDAR and C70 RGB / Astra RGB-D camera options
- Optional MR20 77 GHz mmWave radar with 20 m range and multi-target tracking
- Development resources for SLAM, Nav2, perception, AI interaction, path planning, sensor fusion, and secondary development
Product Overview
The LSV550 combines interchangeable mobile-base architectures with a common ROS development ecosystem. The three chassis types allow students and researchers to compare different mobility principles while keeping a consistent sensing, computing, and software stack.
Standard, Pro, and Ultra packages scale from basic RGB + LiDAR navigation to RGB-D perception, voice interaction, M10P LiDAR, touchscreen control, and MR20 mmWave radar. This makes the platform suitable for progressive teaching from basic chassis control through multi-sensor autonomous systems and AI-enabled robotics.
Chassis Comparison
| Specification | 4WD | Mecanum Wheel | Ackermann |
|---|---|---|---|
| Drive Structure | 4WD with swing-arm suspension | 4-wheel Mecanum with swing-arm suspension | Ackermann structure, no suspension |
| Wheels | 65 mm specialized 4WD wheels | 75 mm aluminum-alloy Mecanum wheels | 65 mm rubber wheels |
| Dimensions | 260 × 214 × 223 mm | 260 × 229 × 228 mm | 280 × 190 × 226 mm |
| Net Weight | 4.0 kg | 4.2 kg | 3.3 kg |
| Payload Capacity | 4 kg | ||
| Maximum Speed | 1 m/s | ||
| Motor | MG513 metal gear reduction motor | ||
| Encoder | 500-line AB-phase GMR encoder | ||
| Endurance | 5 h light load / 3.5 h with 1 kg load | ||
| Low-Level Controller | STM32F407VET6 | ||
Standard, Pro & Ultra Versions
| Specification | Standard | Pro | Ultra |
|---|---|---|---|
| Camera | RGB camera | RGB + depth camera | RGB + depth camera |
| LiDAR | N10P | N10P | M10P |
| Voice / Audio | — | M260C microphone array module | M260C smart speaker |
| Touchscreen | — | — | Included |
| Multi-Mode Joystick | — | — | Included |
| MR20 mmWave Radar | — | — | Included |
ROS Controller Options
| Controller | Raspberry Pi 5 | Jetson Nano | Jetson Orin Nano Super | Jetson Orin NX Super |
|---|---|---|---|---|
| Memory | 4 GB / 8 GB | 4 GB LPDDR4 | 8 GB LPDDR5 | 16 GB LPDDR5 |
| Storage | 64 GB MicroSD | 64 GB MicroSD | 256 GB SSD | 256 GB SSD |
| ROS System | ROS 1 Melodic / ROS 2 Humble | ROS 1 Melodic / ROS 2 Galactic | ROS 1 Noetic / ROS 2 Humble | ROS 1 Noetic / ROS 2 Humble |
| AI Performance | 0.8 TOPS (FP16) | 0.5 TOPS | 67 TOPS | 157 TOPS |
| Network | Gigabit Ethernet, Wi-Fi 802.11ac | Gigabit Ethernet, M.2 Key E | Gigabit Ethernet, M.2 PCIe | Gigabit Ethernet, M.2 PCIe |
Raspberry Pi 5 is suitable for ROS teaching, mapping, and navigation. NVIDIA Jetson options add GPU resources for vision, object detection, multimodal AI, local-model workflows, and other accelerated perception tasks.
2D LiDAR Options
| Specification | N10P | M10P |
|---|---|---|
| Detection Radius | 25 m | 30 m |
| Scanning Frequency | 6–12 Hz adjustable | 12 Hz |
| Sampling Rate | 5400 Hz | 20000 Hz |
| Output Data | Angle, distance, intensity | Angle, distance |
| Angular Resolution | 0.4°–0.8° adjustable | 0.22° |
| Ambient Light Resistance | 60 kLux | 100 kLux |
| Interface | Serial | |
| Scanning Range | 360° | |
| Ranging Principle | TOF (Time of Flight) | |
RGB & RGB-D Camera Options
Standard configurations use a C70 wide-FOV RGB camera. Pro and Ultra configurations add an Astra-series RGB-D depth camera. The dual-camera architecture allows RGB imaging to be used for standard visual workflows while the depth camera supports depth-based following, KCF tracking, AR-tag recognition, visual mapping, and other 3D-perception tasks.
Astra RGB-D Depth Camera
| Depth Resolution | Up to 640 × 480 | Depth Frame Rate | Up to 30 fps |
| RGB Resolution | Up to 640 × 480 | RGB Frame Rate | Up to 30 fps |
| RGB FOV | 63.1° × 49.4° | Depth Range | 0.6–4 m |
| Depth FOV | 58.4° × 45.5° | Dimensions | 165 × 40 × 30 mm |
| Structure | Monocular structured light + monocular RGB | Interface | USB 2.0 |
C70 Wide-FOV RGB Camera
| Resolution | 720P | Driver | UVC plug-and-play |
| FOV | H85° / V47° | Interface | ZH1.5-4PIN to USB 2.0 |
| Frame Rate | 30 fps | Dimensions | 47 × 38 × 28.5 mm |
| Sensor | CMOS | Net Weight | 50 g |
MR20 77 GHz mmWave Radar
The Ultra configuration includes the MR20 millimeter-wave radar for supplementary obstacle perception and moving-target sensing. The radar reports target distance, direction, and speed information and can be combined with LiDAR data in multi-sensor obstacle-avoidance workflows.
| Radar Frequency | 77 GHz | Detection Range | 0.10–20 m |
| Range Accuracy | ±0.1 m | Angular Accuracy | ±1° |
| Speed Range | -60 to +60 km/h | Speed Resolution | 0.36 m/s |
| Speed Accuracy | ±0.18 m/s | Measurement Cycle | 40 ms |
| Azimuth Beam | 120° | Elevation Beam | 120° |
| Maximum Moving Targets | 16 | Supply Voltage | 9–24 V DC |
ROS, AI & Development Resources
The development library includes RTAB-Map, classic 2D LiDAR SLAM, ORB-SLAM2, ROS Qt tools, YOLO object detection, LLM integration, depth-based following, KCF tracking, AR-tag recognition, RRT autonomous exploration, browser camera monitoring, RGB line following, LiDAR following, LiDAR angle masking, voice-oriented workflows, TEB / DWA path planning, DWB / MPPI / RPP controllers, coverage planning, chassis-kinematics examples, path recording and playback, URDF models, mobile tools, hardware-parameter tools, Bluetooth code flashing, multi-sensor fusion examples, and optional 4G / 5G communication.
Voice functions require a compatible microphone / speaker configuration. Local AI-model workloads require compatible onboard compute and an appropriate model size.
Which Configuration Should I Choose?
| Learning / Research Goal | Configuration Guidance |
|---|---|
| Basic ROS and mobile-robot control | Standard + Raspberry Pi 5 |
| 2D SLAM and Nav2 | N10P with Raspberry Pi 5 or Jetson |
| RGB-D vision and visual SLAM | Pro or Ultra |
| Voice interaction and semantic workflows | Pro / Ultra voice-equipped configuration |
| mmWave + LiDAR sensor fusion | Ultra |
| Local AI models and GPU-accelerated perception | Jetson Orin Nano Super or Orin NX Super |
| Mobility / kinematics comparison | Choose 4WD, Mecanum, or Ackermann according to the course or research objective |
Applications
- ROS 1 / ROS 2 robotics education
- Mobile-robot kinematics and chassis-control experiments
- 2D SLAM, localization, and Nav2
- RGB-D visual mapping and target tracking
- YOLO object detection and computer vision
- mmWave radar and LiDAR sensor fusion
- Embodied AI, semantic mapping, and semantic navigation
- University laboratories and robotics algorithm validation
Related Products
For standalone 2D ranging, see the M10 / N10 Series 2D TOF LiDAR. For standalone radar sensing, see the MR20 77 GHz mmWave Radar. For mobile manipulation education, see the LSE550A Educational Robot with Robotic Arm.
AI, Perception & ROS Development Features
The LSV550 development library combines ROS mapping, navigation, perception, AI interaction, sensor fusion, mobile-base control, and secondary-development workflows. Voice-dependent functions require compatible 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 microphone and speaker hardware, 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 onboard-compute 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 mobile-base commands, with LiDAR-based emergency-stop behavior for robot-control 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, mapping, navigation, and mobile-base functions for integrated embodied-AI demonstrations and secondary development.
04 High-Precision Motion Sensing
500-Line GMR Encoder Feedback
The LSV550 drivetrain uses MG513 metal gear reduction motors with 500-line AB-phase GMR encoder feedback for wheel-speed measurement, odometry, low-speed navigation, and mobile-robot control experiments.
05 MR20 77 GHz Millimeter-Wave Radar (Ultra Version)
The Ultra configuration includes the MR20 77 GHz mmWave radar for supplementary obstacle perception and moving-target sensing. It supports a 0.10–20 m range, ±0.1 m distance accuracy, ±1° angular accuracy, 120° azimuth and elevation coverage, a 40 ms measurement cycle, and tracking of up to 16 moving targets.
ROS 1 / ROS 2 radar-development resources support obstacle-detection, following, and LiDAR–radar fusion workflows.
06 Dual-Camera Integration & Support
Standard configurations use the built-in RGB camera. Pro and Ultra configurations add an Astra RGB-D depth camera, allowing users to select the sensing mode that fits the application.
Astra RGB-D Depth Camera
Use the depth camera for depth-based following, KCF tracking, AR-tag recognition, visual mapping, and other RGB-D perception workflows.
Built-In RGB Camera
Use the RGB camera for browser monitoring, RGB line following, and general visual-perception tasks.
Open-Source ROS Development Library
The development library includes ROS, mapping, navigation, perception, AI, sensor fusion, mobile-base 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 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 RGB-D depth 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. Multi-Sensor Fusion Obstacle Avoidance
MR20 mmWave radar and LiDAR data can be combined for supplementary obstacle perception and fusion-based navigation research.
27. Hardware Tuning & Parameter App
Android and iOS tools support parameter tuning, gravity-sensing control, and waveform visualization for supported hardware functions.
28. Wireless Code Flashing
Bluetooth tools support wireless code flashing and remote debugging for supported controller workflows.
29. Optional 4G/5G Remote Modules
Optional cellular communication modules support long-range video transmission and remote-control development over compatible mobile networks.
Frequently Asked Questions
What is the LSV550 designed for?
The LSV550 is an educational and research mobile-robot platform for ROS development, SLAM, autonomous navigation, computer vision, mobile-robot control, sensor fusion, AI interaction, and university robotics laboratories.
Which chassis options are available?
The LSV550 is available with 4WD, Mecanum Wheel, and Ackermann chassis configurations.
What motors and encoders are used?
The drivetrain uses MG513 metal gear reduction motors with 500-line AB-phase GMR encoder feedback.
What are the payload and maximum speed?
The platform is specified for a 4 kg payload and a maximum speed of 1 m/s.
What is the difference between Standard, Pro, and Ultra?
Standard uses an RGB camera and N10P LiDAR. Pro adds an Astra RGB-D depth camera and M260C microphone array. Ultra uses RGB + depth cameras, M10P LiDAR, M260C smart speaker, touchscreen, multi-mode joystick, and MR20 mmWave radar.
Which onboard computers are available?
Controller options include Raspberry Pi 5 in 4 GB or 8 GB configurations, Jetson Nano 4 GB, Jetson Orin Nano Super 8 GB, and Jetson Orin NX Super 16 GB. A controller-free option is also available.
Which ROS versions are supported?
Raspberry Pi 5 supports ROS 1 Melodic and ROS 2 Humble. Jetson Nano supports ROS 1 Melodic and ROS 2 Galactic. Jetson Orin Nano Super and Orin NX Super support ROS 1 Noetic and ROS 2 Humble.
What LiDAR options are available?
N10P and M10P 360° TOF LiDAR options are available. N10P provides a 25 m detection radius with adjustable 6–12 Hz scanning, while M10P provides a 30 m detection radius with 12 Hz scanning.
What camera options are available?
Standard configurations use the C70 wide-FOV RGB camera. Pro and Ultra configurations add an Astra-series RGB-D depth camera for depth sensing, visual mapping, following, tracking, and AR-tag workflows.
What does the MR20 mmWave radar add?
The MR20 provides 77 GHz moving-target sensing over a 0.10–20 m range, reports distance, direction, and speed information, and can track up to 16 moving targets. The LSV550 development resources include radar-based obstacle-detection and LiDAR–radar fusion workflows.
Does the LSV550 support sensor fusion?
Yes. The Ultra configuration can combine MR20 mmWave radar and LiDAR data for supplementary obstacle perception and fusion-navigation research.
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 platform support computer vision?
Yes. Development examples include YOLO object detection, RGB-D visual following, KCF tracking, AR-tag recognition, RGB line following, and browser-based camera monitoring.
Does the LSV550 support AI and large-model applications?
Yes. Development workflows include LLM integration, multimodal vision, RAG-oriented interaction, semantic mapping and navigation, object tracking, and local-model deployment on compatible onboard-compute configurations.
Which functions require voice hardware?
Speech interaction, voice wake-up and interruption, sound-source localization, voice-controlled navigation, voice semantic labeling, voice-driven summoning, and text-to-speech workflows require compatible microphone and speaker hardware.
What development resources are included?
Development resources include source code, ROS system images, tutorials, URDF models, mapping and navigation examples, perception examples, AI and sensor-fusion workflows, mobile tools, controller utilities, and secondary-development resources.
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