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ROS Research Robot-LSR550(2WD）
- 100% Open-Source Code
- Professional Technical Support
- Comprehensive Development Resources
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Product Overview
Meet the LSR550 (2WD-Differential Drive)—our flagship ROS robotics platform engineered specifically for advanced research, academia, and prototyping. Built to bridge the gap between high-end industrial utility and accessible engineering, the LSR550 (2WD Differential Drive) truly redefines value by being highly affordable without compromising on real-world capability.
To meet the diverse demands of various research projects, this versatile platform offers modular customization with multiple options for suspension types, speed ratings, and payload capacities. Outfitted with high-performance, cost-effective LiDAR and advanced depth cameras, the LSR550 (2WD Differential Drive) is the ultimate turnkey solution for budget-conscious university labs, educators, and robotics developers looking for maximum ROI.
|
Specification |
Product Details & Parameters |
|
Motor |
MD36L 60W Brushed DC Motor |
|
Motor Gear Ratio |
1:27 |
|
Max Speed |
1.3 m/s |
|
Rated Payload Capacity |
30 kg |
|
Net Weight |
9.4 kg |
|
Obstacle Clearance (Single Right-Angle Step) |
20 mm |
|
Dimensions |
408 × 380.7 × 274.6 mm |
|
Driven / Passive Wheels |
127 mm diameter Omni Wheels |
|
Drive Wheels |
125 mm diameter Solid Rubber Tires |
|
Endurance |
No-load: ~9 hours/ Max 20% Load: ~8 hours |
|
Encoder |
500-line High-Precision GMR (Giant Magnetoresistance) Effect AB-Phase Encoder |
|
Max Climbing Incline |
35° |
|
Electronic Control & Features |
• Basic Package: Supports Serial port, CAN bus, Multi-mode smart controller, and Mobile App control. • ROS Package: Includes advanced Navigation, SLAM Mapping, Obstacle Avoidance, Visual/Video Transmission, etc. |
|
Other Configurations |
Power switch, OLED display, low-level master controller, development manual, video tutorials, full source code, ROS system image, etc. |
ROS Controller Specification Comparison
|
Feature |
Raspberry Pi 5 (8GB) |
Orin Nano Super (8GB) |
Orin NX Super (16GB) |
|
CPU |
ARM Cortex-A76 (Quad-core) @ 2.4GHz |
6-core Arm® Cortex® A78AE v8.2 |
8-core Arm® Cortex® A78AE v8.2 |
|
GPU |
VideoCore VII @ 800MHz |
NVIDIA Ampere w/ 1024 CUDA Cores & 32 Tensor Cores |
NVIDIA Ampere w/ 1024 CUDA Cores & 32 Tensor Cores |
|
AI Performance |
0.8 TOPS (FP16) |
67 TOPS |
157 TOPS |
|
Memory |
4GB / 8GB |
8GB 128-bit LPDDR5 @ 102 GB/s |
16GB 128-bit LPDDR5 @ 102.4 GB/s |
|
USB Interface |
2 * USB 3.0 + 2 * USB 2.0 |
3 * USB 3.0, 1 * USB 2.0, 1 * Type-C |
3 * USB 3.0, 1 * USB 2.0, 1 * Type-C |
|
Video Input |
MIPI CSI |
MIPI CSI |
MIPI CSI |
|
Video Output |
2 * Micro-HDMI (4Kp60) |
1 * HDMI 2.0 |
1 * HDMI 2.0 |
|
Video Encoding |
Not Supported |
1080p30 (CPU-supported) |
H.265 (Up to 1*4K60) |
|
Video Decoding |
H.265 (4Kp60) |
H.265 (Up to 1*4K60) |
H.265 (Up to 1*8K30) |
|
Storage |
64GB MicroSD |
256GB SSD |
256GB SSD |
|
Network |
GigE, Wi-Fi 802.11ac |
GigE, M.2 PCIe |
GigE, M.2 PCIe |
|
GPIO |
40 Pins |
40 Pins |
40 Pins |
|
Rated Power |
25W (5V/5A) |
7W / 15W / 25W Modes |
10W / 15W / 25W / 40W Modes |
|
Power Input |
5V |
9V ~ 19V |
9V ~ 19V |
System images supported by different ROS
|
Controller |
System Image |
|
Raspberry Pi 5 |
ROS1 melodic, ROS2 humble |
|
Orin Nano Super (8GB) |
ROS1 noetic, ROS2 humble |
|
Orin NX Super (16GB) |
ROS1 noetic, ROS2 humble |
LiDAR Specification Comparison
|
Specification |
N10P |
M10P |
|
Detection Radius |
25m |
30m |
|
Scanning Frequency |
6 ~ 12Hz (Adjustable) |
12Hz |
|
Sampling Rate |
5400Hz |
20000Hz |
|
Output Data |
Angle, Distance, Intensity |
Angle, Distance |
|
Angular Resolution |
0.4° ~ 0.8° (Adjustable) |
0.22° |
|
Ambient Light Resistance |
60KLux (Outdoor use supported) |
100KLux (Outdoor use supported) |
|
Interface Type |
Serial Port |
|
|
Drive Motor Type |
Brushless Motor |
|
|
360°Scanning Range |
○ |
|
|
LiDAR Principle |
TOF (Time of Flight) |
|
The Multi-Line LiDAR comes standard on the Ultra version.
Multi-Line LiDAR Integration: Designed for low-speed autonomous vehicles, our 16-line mechanical LiDAR utilizes TOF (Time-of-Flight) ranging.
If you would like to equip other versions with a multi-line LiDAR (16-line/32-line), please contact us.
RGBD Depth Camera Specification
|
Feature |
Specification |
Feature |
Specification |
|
Ranging Principle |
Monocular Structured Light (Infrared Projection) |
Depth Range |
0.6 – 8 m |
|
Depth Resolution @ Frame Rate |
640 × 480 @ 30fps |
Data & Power Interface |
USB 2.0 Type-A |
|
Depth Field of View (FOV) |
H58.4° × V45.5° |
Operating Environment |
Indoor |
|
RGB Resolution @ Frame Rate |
1920 × 1080 @ 30fps |
Power Consumption |
< 2.5W |
|
RGB Field of View (FOV) |
H66.1° × V40.2° |
Laser Safety Rating |
Class 1 Laser Product |
01 AI Large Model Integration(Requires Voice Module)
- Large Language Models (LLM)：Supports real-time access to major AI model platforms, enabling deep semantic parsing of text commands and flexible, intelligent conversational responses.
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Large Voice Models：Powered by AI voice models, featuring real-time speech-to-text and text-to-speech conversion. Combined with on-board voice modules and speakers, intelligent interaction is more seamless than ever.
- Large Vision Models：Leverages high-definition cameras to rapidly analyze and comprehend image content, precisely identify object categories, and provide both text and voice feedback.
- Local Model Deployment：Supports local deployment of large models for low-latency AI inference. By eliminating cloud dependency, the autonomous decision-making capabilities of the intelligent device are significantly enhanced.
02 AI Multi-modal Large Model Applications(Requires Voice Module)
- AI Vision: Scene Understanding & Perception： Analyzes images/videos via vision large models to identify environmental elements and relationships, providing cognitive feedback.
- AI Recognition: Object Tracking：Locks onto targets through vision model analysis to perform real-time dynamic tracking.
- AI Interaction: Color-Aware Line Following：Uses Large Language Models (LLM) to identify color information for interaction, precisely sensing lines for path following.
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Flexible Chassis Control：AI-empowered chassis control with LiDAR-based emergency stop capabilities for safe and intelligent maneuvering.
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AI Interaction: LiDAR Tracking：LLM-activated LiDAR tracking for seamless transitions between convenient control and precision tracking.
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Built-in RAG + Dual-Model Inference Architecture：Retrieves semantic fragments from knowledge bases; LLMs handle task decomposition and step planning, while multi-modal models convert instructions into executable Vision-Action closed-loop commands.
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Voice Wake-up & Interruption Support：Allows voice commands to interrupt ongoing operations to receive and process new dialogue instructions.
03 Embodied AI SLAM Mapping & Semantic Navigation
- AI Multi-modal Large Model + Semantic Control Mapping：Understands semantics through AI multi-modal large models. Once mapping is activated, the robot is controlled via semantic commands to scan and save maps of the area.
- AI Multi-modal Large Model + Semantic Navigation：Set semantic coordinate files within the pre-built map. During navigation, these are loaded automatically; the robot understands semantic meanings and assigns navigation target points accordingly.
- AI Multi-modal Large Model + Voice Semantic Labeling：Add new semantic coordinate labels via voice dialogue during navigation without pre-writing configuration files. Low environmental requirements with high labeling flexibility.
- AI Multi-modal Large Model + Full-Function Combined Application：Leveraging AI multi-modal voice interaction, all robot functions can be combined without relying on specific environment setups. Supports auto-start of interactive nodes, allowing users to master ROS functions with zero coding for a quick start.
04 High-Precision Motion Sensing
-
GMR High-Precision Encoders: Features the newly upgraded 500-line AB-phase GMR encoder. With precision levels over 38 times higher than standard Hall-effect encoders (the market standard), these encoders ensure exceptional stability and performance during low-speed navigation and fine maneuvering.
|
Feature |
Specification |
Feature |
Specification |
|
Motor Model |
MD36L P27 |
Motor Voltage |
24V DC |
|
Rated Power |
60W |
No-load Speed |
310 ± 12%rpm |
|
Rated Speed |
260 ± 12%rpm |
Rated Torque |
10kg.cm |
|
Stall Torque |
64kg.cm(Min) |
No-load Current |
0.4A (Max) |
|
Rated Current |
2.9A (Max) |
Stall Current |
22.1A (Max) |
05 Outdoor Navigation & LiDAR Perception (Optional)
Multi-Line LiDAR Integration: Designed for low-speed autonomous vehicles, our 16-line mechanical LiDAR utilizes TOF (Time-of-Flight) ranging.
Note: The Multi-Line LiDAR comes standard on the Ultra version.
Autoware Outdoor Mapping & Navigation
Complex Path Planning: Achieve advanced navigation using generated High-Definition (HD) maps.
Point Cloud Obstacle Avoidance: Real-time navigation safety powered by LiDAR point cloud clustering.
Recommended Configuration: For optimal Autoware performance, we recommend the NVIDIA Orin-series controller paired with 16/32-line LiDAR.
Pro Tip: For debugging efficiency, we suggest adding an additional chassis expansion layer and an integrated display screen.(If you need to add an additional chassis, please contact us.)
06 Autonomous Power Management (Optional)
Key Feature Introduction (Fully Open Source)
1.RTAB-Map 3D VSLAM & LiDAR Integration
Supports RTAB-Map pure vision mapping and LiDAR-vision fusion mapping. Fully compatible with ROS 1 and ROS 2 for versatile 3D environment reconstruction.
2.Classic 2D LiDAR Mapping & Navigation
ROS 2: Supports Gmapping, Cartographer, and slam_toolbox.
ROS 1: Supports Gmapping, Hector, Karto, and Cartographer.
Navigation: Features autonomous point-to-point navigation, multi-point waypoints, and dynamic obstacle avoidance.
3.ORB-SLAM2 Visual Mapping
Features the open-source ORB-SLAM2 framework for real-time camera pose
estimation and sparse 3D reconstruction. Provides real-scale metric information when used in RGB-D mode.
4.ROS QT Graphical User Interface(GUI)
Deployed with a dedicated QT-based GUI for "one-click" ROS activation. Provides
intuitive real-time feedback on robot velocity, battery status, and system health.
5.YOLO Object Detection
ROS 1: Powered by YOLO.v3 for general object, traffic sign, and gesture recognition.
ROS 2: Powered by YOLO.v8 and YOLOv11 for state-of-the-art object detection and custom model training support.
6.LLM Deployment
Supports both offline local deployment and online cloud-based integration of Large
Language Models for advanced reasoning and interaction.
7.Depth-Based Visual Following
Utilizes depth cameras to calculate target distance and bearing for smooth, real-time robot following.
8.KCF Target Tracking
Employs Kernelized Correlation Filters (KCF) via the depth camera to identify and track objects with fixed visual features.
9.AR Tag Recognition & Following
Detects and tracks the 6-DOF pose of AR Tags, allowing for tag-following behaviors and expanded tag-based localization.
10.RRT Autonomous Exploration
Enables fully autonomous mapping using the RRT algorithm. The robot explores, maps, saves the data, and returns to the starting point without human intervention.
11.Web-Based Camera Monitoring
Remotely view the robot's live camera feed through any PC browser for quick deployment of remote surveillance tasks.
12.RGB Camera Line Following
Enables the robot to follow ground lines via RGB vision. Integrated with LiDAR to ensure automatic obstacle avoidance during line-following missions.
13.LiDAR-Based Following
Scans the environment for nearby obstacles and intelligently selects the nearest target for the robot to follow.
14.LiDAR Angle Masking
Optimized via SDK to allow custom angle shielding/masking for all supported LiDAR models.
15.Sound Source Localization
Uses a microphone array to achieve sound source localization with 1° precision. This technology facilitates advanced noise reduction and voice-driven navigation.
16.Voice-Activated Summoning
Call the robot to your location from anywhere. It identifies the user's direction via the mic array and determines distance using LiDAR.
17.Voice-Controlled Navigation
Semantic analysis of voice commands allows the robot to navigate to any predefined point on the saved map.
18.TTS (Text-to-Speech) Interaction
Enables full human-machine interaction through TTS technology, with support for expanded iFLYTEK online voice dialogue features.
19.TEB & DWA Path Planning
Includes detailed video tutorials and Python-based "mini-games" to help users learn navigation path planning from the ground up.
20.DWB / MPPI / RPP Controllers
Provides three ready-to-use controller plugins (DWB, MPPI, and RPP) tailored for different scenarios and various robot footprints.
21.Full Coverage Path Planning
Automatically generates a path that covers an entire user-specified area, ideal for cleaning or inspection robots.
22.Chassis Kinematics Analysis
Provides comprehensive kinematics analysis for various chassis types: Ackermann, Differential, Tracked, Mecanum, Omnidirectional, and 4WD.
23.Path Recording & Playback
Record manual trajectories and reproduce them autonomously using the Nav2 navigation framework.
24.Comprehensive URDF Models
Includes high-fidelity URDF models that accurately match the physical robot's dimensions and properties.
25.ROS Mobile APP for Navigation
A dedicated app for controlling the ROS environment, supporting motion control, mapping, and navigation tasks.
26.Hardware Tuning & Parameter APP
Supports Android and iOS. Features real-time parameter tuning, gravity-sensing control, and waveform visualization.
27.Wireless Code Flashing (Bluetooth)
Standard Bluetooth module allows for remote debugging and "second-level" code flashing via the dedicated host computer, simplifying secondary development.
28.Optional 4G/5G Remote Modules
Supports optional 4G/5G modules for long-range video transmission and remote control over cellular networks.
29.3D Reconstruction for Real-World Autonomous Driving
Upgrade with an optional multi-line LiDAR to unlock outdoor 3D mapping and environment reconstruction, bringing your development platform incredibly close to full-scale autonomous vehicles.
30.Autoware.universe Multi-Point Navigation Routines
Features pre-configured Autoware.universe multi-point navigation examples. By utilizing Python scripts to dispatch sequential waypoint commands, the system achieves smooth, continuous multi-point autonomous navigation across complex routes.
1. Free Shipping Policy
We offer free standard shipping on orders.
2. Customs & Import Fees
All international shipments are subject to local customs regulations. Import duties, taxes, or other fees may be charged by your country's customs authority upon arrival.
Our shipping terms are based on Ex-Works (EXW) or FCA (Origin), which means any import duties, taxes, or local customs fees are the sole responsibility of the customer. While our carriers provide basic assistance with the clearance process, we are not responsible for delays or costs imposed by your local authorities.
Important: If a package is returned to us due to unpaid customs duties or a refusal to clear customs, any original shipping fees, return shipping costs, and related handling charges will be deducted from your refund.
3. Order Processing Time
Orders are typically processed within 24 hours after placement and shipped the next business day.
Orders placed on weekends or public holidays will be shipped on the next working day.
Once shipped, you will receive a confirmation email with tracking information.