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LSR550 ACM Ackermann ROS 2 UGV Research Platform
- 100% Open-Source Code
- Professional Technical Support
- Comprehensive Development Resources
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The LSR550 ACM is an Ackermann-steering ROS 2 UGV research platform for autonomous driving, SLAM, navigation, perception, sensor fusion, AI, and mobile-robot control. Three chassis configurations cover payload requirements from 25 kg to 45 kg, while Raspberry Pi 5 and NVIDIA Jetson options support workloads ranging from standard ROS development to GPU-accelerated perception and Autoware research.
- Ackermann steering geometry for vehicle-like motion and autonomous-driving research
- Three chassis configurations with 25 kg, 45 kg, and 35 kg payload capacities
- MD36L 60 W or MD60 100 W drive motors depending on chassis configuration
- 500-line AB-phase GMR encoder feedback
- Raspberry Pi 5, Jetson Orin Nano Super, and Jetson Orin NX Super controller options
- N10P / M10P 2D TOF LiDAR plus RGB-D depth-camera configurations
- ROS 1 / ROS 2 resources for SLAM, Nav2, perception, planning, tracking, and secondary development
- Optional multi-line LiDAR, Autoware workflows, AI integration, semantic navigation, and autonomous charging
Product Overview
Unlike differential-drive or omnidirectional platforms, the LSR550 ACM uses Ackermann steering geometry. This makes it suitable for research involving vehicle-like steering, trajectory tracking, autonomous-driving algorithms, outdoor navigation, and motion planning under nonholonomic constraints.
Standard, Pro, and Ultra versions are available across three chassis families. The Basic Package provides the robot chassis without the ROS computer, LiDAR, or depth-camera bundle, while ROS configurations add the selected onboard computer and perception hardware.
Chassis Configuration Comparison
| Specification | Premium Oscillating Axle | Ultimate Oscillating Axle | Independent Suspension |
|---|---|---|---|
| Motor | MD36L 60 W brushed DC | MD60 100 W DC gear motor | MD60 100 W DC gear motor |
| Reduction Ratio | 1:27 | 1:18 | 1:18 |
| Maximum Speed | 1.3 m/s | 1.65 m/s | 1.65 m/s |
| Payload | 25 kg | 45 kg | 35 kg |
| Net Weight | 9.3 kg | 20.0 kg | 43.6 kg |
| Maximum Climbing Angle | 27° | 25° | 11° |
| Single Right-Angle Step Clearance | 30 mm | 30 mm | 75 mm |
| Dimensions | 445 × 360 × 206 mm | 774 × 570 × 227 mm | 881 × 766 × 432 mm |
| Minimum Turning Radius | 0.8 m | 1.65 m | 1.78 m |
| Endurance | 9.5 h no-load / 8.5 h at 20% load | 4.5 h no-load / 3 h at 20% load | 13 h no-load / 9 h at 20% load |
| Steering Servo | S20F 20 kg digital servo | DS5160 60 kg digital servo | DS5160 60 kg digital servo |
| Wheels | 125 mm solid rubber | 180 mm solid rubber | 254 mm pneumatic rubber |
| 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 |
| AI Compute | 0.8 TOPS (FP16) | 67 TOPS | 157 TOPS |
Raspberry Pi 5 is suitable for ROS education, SLAM, navigation, and standard mobile-robot development. NVIDIA Jetson configurations provide additional GPU resources for object detection, multimodal perception, local AI models, multi-sensor 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 |
| Interface | Serial | |
| 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 version includes multi-line LiDAR as standard. Optional 16-line and 32-line mechanical LiDAR configurations support 3D point-cloud perception, mapping, obstacle detection, environment reconstruction, and low-speed autonomous-driving research.
Autoware-oriented workflows include HD-map-based path planning, point-cloud obstacle avoidance, 3D reconstruction, and multi-point navigation. Jetson Orin controllers provide the compute resources typically used for these 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, optional 4G / 5G communication, 3D reconstruction, and Autoware examples.
The package also includes source code, ROS system images, development manuals, video tutorials, mapping examples, navigation examples, and sensor-integration resources for secondary development.
Which Configuration Should I Choose?
| Research Goal | Configuration Guidance |
|---|---|
| ROS learning, SLAM & Nav2 | Raspberry Pi 5 with 2D LiDAR |
| Computer vision & AI perception | Jetson Orin Nano Super or Orin NX Super |
| Autoware & 3D point-cloud research | Jetson Orin configuration with multi-line LiDAR |
| Custom sensor / computer integration | Basic Package with your own compute and sensing stack |
| Higher payload expansion | Ultimate Oscillating Axle configuration |
| Larger wheels and 75 mm step clearance | Independent Suspension configuration |
Applications
- Ackermann vehicle-control and trajectory-tracking research
- ROS 2 SLAM, localization, and Nav2 development
- Autonomous-driving and Autoware research
- LiDAR-camera and multi-sensor fusion
- Computer vision, target detection, and tracking
- Embodied AI and semantic navigation
- Outdoor mapping and 3D point-cloud perception
- University robotics education and algorithm validation
Related Products
For a 4WD skid-steer research chassis, see the LSR550 4WD ROS Research Robot. For omnidirectional motion, see the LSR550 Omni ROS Research Robot. For a compact differential-drive version, see the LSR550 2WD ROS Research Robot.
AI, Perception & ROS Development Features
The LSR550 ACM development library combines Ackermann vehicle control, ROS navigation, perception, AI integration, outdoor autonomy, and human–robot interaction 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 Ackermann steering and speed 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, mapping, navigation, and Ackermann motion functions for integrated embodied-AI demonstrations and secondary development.
04 High-Precision Motion Sensing
500-Line GMR Encoder Feedback
All chassis configurations use 500-line AB-phase GMR encoders for wheel-speed and odometry feedback.
Motor note: the image above shows the Premium MD36L P27 configuration. Premium uses a 60 W MD36L motor with a 1:27 reduction ratio, while the Ultimate and Independent Suspension variants use 100 W MD60 motors with a 1:18 reduction ratio.
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 detection, 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.
Jetson 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, vehicle 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.
Does the LSR550 ACM support ROS 2?
Yes. ROS 2 Humble system images are available for Raspberry Pi 5, Jetson Orin Nano and Jetson Orin NX configurations.
Does it support Nav2?
Yes. The platform supports Nav2-based mapping, localization, path planning and autonomous navigation.
Can I use Autoware with this platform?
Yes. Autoware-based mapping and navigation configurations are available. For heavier perception workloads, we recommend an NVIDIA Orin controller with multi-line LiDAR.
Can I add my own sensors?
Yes. The platform is designed for secondary development and can integrate additional LiDAR, cameras, IMUs, GNSS and other research sensors.
Do you provide URDF and source code?
Yes. Development resources include URDF models, source code, tutorials and ROS system images.
Which configuration should I choose?
For standard ROS navigation and education, Raspberry Pi configurations are suitable. For computer vision, AI and Autoware development, NVIDIA Jetson configurations are recommended.
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