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LSR550 4WD ROS Research Robot Platform
- 100% Open-Source Code
- Professional Technical Support
- Comprehensive Development Resources
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The LSR550 4WD is a four-wheel-drive ROS research robot platform for SLAM, autonomous navigation, computer vision, embodied AI, mobile-robot control, and low-speed autonomous-driving research. Two chassis options—oscillating-axle suspension and independent suspension—provide different mechanical layouts while sharing a 25 kg rated payload, 6-inch off-road wheels, MD36L 60 W drive motors, and 500-line GMR encoder feedback.
- 4WD mobile robot platform with two suspension configurations
- 25 kg rated payload and up to 1.82 m/s maximum speed
- MD36L 60 W brushed DC motors with 1:27 gear ratio
- 500-line AB-phase GMR encoders for wheel-speed and odometry feedback
- Raspberry Pi 5, Jetson Orin Nano Super, and Jetson Orin NX Super controller options
- N10P / M10P 2D TOF LiDAR and RGB-D depth-camera configurations
- ROS 1 / ROS 2 resources for SLAM, Nav2, perception, tracking, path planning, and secondary development
- AI, semantic navigation, optional multi-line 3D LiDAR, Autoware, and autonomous-charging workflows
Product Overview
The LSR550 4WD is designed for university laboratories, robotics courses, autonomous-navigation research, perception research, and mobile-robot prototyping. Standard, Pro, and Ultra configurations are available with multiple onboard-computer options. The Basic Package provides the mobile platform without the ROS computer, LiDAR, or depth-camera bundle.
The Ultra configuration includes multi-line LiDAR as standard for 3D point-cloud perception and outdoor autonomous-driving research. Voice-driven AI functions require compatible voice hardware, while local AI-model workloads depend on the selected onboard computer and model requirements.
Chassis Options
| Specification | Oscillating Axle Suspension | Independent Suspension |
|---|---|---|
| Net Weight | 7.6 kg | 10.7 kg |
| Dimensions | 465 × 385 × 218 mm | 580 × 530 × 234 mm |
| Maximum Climbing Incline | 25° | 20° |
| Single Right-Angle Step Clearance | 55 mm | 45 mm |
| Suspension Layout | Oscillating axle | Independent suspension with shock absorption |
| Drive Wheels | 6-inch (152 mm) off-road wheels | |
| Maximum Speed | 1.82 m/s | |
| Rated Payload | 25 kg | |
| Endurance | Approx. 8 h no-load / approx. 6.5 h with 3 kg payload | |
| Motor & Gear Ratio | MD36L 60 W brushed DC motor, 1:27 | |
| Encoder | 500-line AB-phase GMR encoder | |
Onboard Computer Options
| Controller | Raspberry Pi 5 | Jetson Orin Nano Super | Jetson Orin NX Super |
|---|---|---|---|
| Memory | 8 GB | 8 GB LPDDR5 | 16 GB LPDDR5 |
| Storage | 64 GB MicroSD | 256 GB SSD | 256 GB SSD |
| ROS System | ROS 1 Melodic / ROS 2 Humble | ROS 1 Noetic / ROS 2 Humble | ROS 1 Noetic / ROS 2 Humble |
| Network | Gigabit Ethernet, Wi-Fi 802.11ac | Gigabit Ethernet, M.2 PCIe | Gigabit Ethernet, M.2 PCIe |
Raspberry Pi 5 is suitable for ROS education, SLAM, and standard navigation workloads. NVIDIA Jetson configurations add GPU resources for computer vision, AI perception, multimodal interaction, 3D LiDAR processing, and Autoware-oriented research.
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 |
| Scanning Range | 360° | |
| Ranging Principle | TOF (Time of Flight) | |
RGB-D Depth Camera
| Ranging Principle | Monocular structured light with infrared projection |
| Depth Range | 0.6–8 m |
| Depth Resolution / Frame Rate | 640 × 480 @ 30 fps |
| RGB Resolution / Frame Rate | 1920 × 1080 @ 30 fps |
| Depth FOV | H58.4° × V45.5° |
| RGB FOV | H66.1° × V40.2° |
| Interface | USB 2.0 Type-A |
| Operating Environment | Indoor |
| Power Consumption | < 2.5 W |
| Laser Safety | Class 1 laser product |
Ultra Configuration: 3D LiDAR & Autoware
The Ultra configuration includes multi-line LiDAR as standard. The 16-line mechanical LiDAR uses TOF ranging, provides 360° horizontal perception, and supports point-cloud mapping, obstacle perception, 3D reconstruction, and outdoor autonomous-driving research. Other configurations can also be equipped with 16-line or 32-line LiDAR.
Autoware development workflows include HD-map-based path planning, point-cloud obstacle avoidance, 3D environment reconstruction, and multi-point navigation. NVIDIA Orin controllers are suitable for these higher-compute perception and planning workloads.
ROS, AI & Development Resources
Development resources include RTAB-Map, 2D LiDAR SLAM, ORB-SLAM2, ROS Qt tools, YOLO object detection, LLM integration, depth-based following, KCF tracking, AR-tag recognition, RRT autonomous exploration, RGB line following, LiDAR following, angle masking, voice-interaction workflows, TEB / DWA path planning, Nav2 DWB / MPPI / RPP controllers, coverage planning, path recording and playback, URDF models, mobile tools, Bluetooth code flashing, optional 4G / 5G communication, 3D reconstruction, and Autoware examples.
The ROS package includes development manuals, video tutorials, source code, and ROS system images for secondary development.
Optional Autonomous Charging
Autonomous-charging workflows support return-to-station and docking using ROS mapping and navigation. Development resources cover charging-station calibration, docking methods, and low-battery return logic.
Applications
- ROS 1 / ROS 2 robotics education and research
- 2D and 3D SLAM
- Nav2 autonomous navigation and path planning
- Computer vision, target detection, and tracking
- Embodied AI and semantic navigation
- Autoware and low-speed autonomous-driving research
- Suspension, chassis-control, and mobile-robot kinematics research
- Secondary development and algorithm validation
Related Products
For a compact differential-drive platform, see the LSR550 2WD ROS Research Robot. For omnidirectional motion, see the LSR550 Omni ROS Research Robot. For mobile manipulation, see the LSR550A Mobile Manipulation Robot.
AI, Perception & ROS Development Features
The LSR550 4WD development library combines ROS navigation, perception, AI integration, interaction, outdoor autonomy, and mobile-robot control 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 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 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 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 Sensing
500-Line GMR Encoder Feedback
The drivetrain uses 500-line AB-phase GMR encoders for wheel-speed and odometry feedback, supporting low-speed navigation and fine maneuvering.
| Motor Parameter | Specification |
|---|---|
| Motor Model | MD36L P27 |
| Motor Voltage | 24 V DC |
| Rated Power | 60 W |
| No-Load Speed | 310 ± 12% rpm |
| Rated Speed | 260 ± 12% rpm |
| Rated Torque | 10 kg·cm |
| Stall Torque | 64 kg·cm minimum |
| No-Load Current | 0.4 A maximum |
| Rated Current | 2.9 A maximum |
| Stall Current | 22.1 A maximum |
05 Outdoor Navigation & 3D LiDAR Perception (Optional)
Multi-Line LiDAR Integration
Optional 16-line and 32-line mechanical LiDAR configurations support 360° horizontal 3D perception, point-cloud mapping, obstacle perception, and autonomous-driving research. The Ultra configuration 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
Autonomous-charging workflows support return-to-station and docking using ROS mapping and navigation. Development resources include charging-station calibration, docking methods, and low-battery return logic.
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 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. 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 optional 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 LSR550 4WD designed for?
The LSR550 4WD is designed for ROS research, SLAM, autonomous navigation, computer vision, embodied AI, mobile-robot control, and low-speed autonomous-driving development.
What chassis options are available?
Two 4WD chassis options are available: an oscillating-axle suspension version and an independent-suspension version. Both support a 25 kg rated payload and use 6-inch off-road wheels.
What are the main differences between the two suspension versions?
The oscillating-axle version weighs 7.6 kg, measures 465 × 385 × 218 mm, climbs inclines up to 25°, and clears a 55 mm single right-angle step. The independent-suspension version weighs 10.7 kg, measures 580 × 530 × 234 mm, climbs inclines up to 20°, and clears a 45 mm step.
What motors and encoders are used?
The platform uses MD36L 60 W brushed DC motors with a 1:27 gear ratio and 500-line AB-phase GMR encoders for wheel-speed and odometry feedback.
How fast can the LSR550 4WD travel?
The maximum speed is 1.82 m/s.
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?
N10P and M10P 360° TOF LiDAR options are available for 2D SLAM and navigation. The Ultra configuration includes multi-line LiDAR as standard, and 16-line or 32-line LiDAR can also be used for 3D perception workflows.
Does the robot support RGB-D vision?
Yes. ROS controller packages support an RGB-D depth camera for visual SLAM, depth-based following, target tracking, AR-tag recognition, and other perception workflows.
Can I use Autoware with this platform?
Yes. Autoware-oriented workflows include HD-map-based path planning, point-cloud obstacle avoidance, 3D reconstruction, and multi-point navigation. NVIDIA Orin controllers with 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 platform 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?
Autonomous-charging workflows are available for return-to-station and docking using ROS mapping and navigation, including charging-station calibration and low-battery return logic.
What development resources are included?
Development resources include source code, ROS system images, development manuals, video tutorials, navigation and perception examples, URDF models, controller tools, AI integration examples, mobile tools, and secondary-development resources.
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