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LSE550A Educational ROS Mobile Manipulator with Robotic Arm
- 100% Open-Source Code
- Professional Technical Support
- Comprehensive Development Resources
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The LSE550A is an educational ROS mobile manipulator that combines a mobile robot base with a 4-DOF or 6-DOF robotic arm for navigation, perception, manipulation, and AI research. It supports MoveIt, RViz, LiDAR SLAM, RGB-D vision, Nav2, object detection, target tracking, and secondary development across Raspberry Pi and NVIDIA Jetson controller options.
- Three model options: 4DOF Arm + Mecanum Wheel, 4DOF Arm + 4WD, and 6DOF Arm + Mecanum Wheel
- MoveIt and RViz support for robotic-arm kinematics, path planning, Cartesian trajectories, and collision-aware manipulation
- 1:1-scale URDF model for simulation, visualization, and motion-planning development
- Raspberry Pi 5, Jetson Nano, Jetson Orin Nano Super, and Jetson Orin NX Super controller options
- N10P or M10P 360° TOF LiDAR, with optional touchscreen configurations
- ROS 1 / ROS 2 resources for SLAM, Nav2, vision, target tracking, autonomous exploration, and mobile manipulation
- Robotic-arm examples including adaptive gripping, color sorting, color tracking, face tracking, and gesture recognition
- Optional multi-line LiDAR workflows for outdoor 3D reconstruction and autonomous-driving research
Product Overview
The LSE550A is designed for courses and research projects that require both autonomous mobile-robot functions and robotic-arm manipulation. The platform combines navigation, perception, motion planning, and grasping workflows in one ROS development environment.
Users can choose a 4-DOF or 6-DOF robotic arm. The 6-DOF configuration adds a horizontal rotating base and a rotating gripper servo for additional dexterity. It also includes an additional RGB camera to support manipulation workflows when the gripper moves outside the primary depth camera field of view.
Model Options
| Model | Mobile Base | Robotic Arm | Use Case |
|---|---|---|---|
| 4DOF-Arm - Mecanum Wheel | Mecanum | 4 DOF | Mobile manipulation, SLAM, vision, grasping, and multidirectional base-motion experiments |
| 4DOF-Arm - 4WD | 4WD | 4 DOF | Mobile manipulation and navigation with a conventional 4-wheel drive base |
| 6DOF-Arm - Mecanum Wheel | Mecanum | 6 DOF | Expanded manipulation, object sorting, and more complex arm-motion workflows |
MoveIt, RViz & URDF Development
The platform includes a 1:1-scale URDF model for RViz visualization and MoveIt-based motion planning. This allows students and developers to work with robotic-arm kinematics, planning scenes, interactive control, and simulated motion before or alongside physical execution.
Advanced Motion & Path Planning
- Interactive Control: drag-and-drop arm manipulation and preset-position execution through RViz workflows
- Cartesian Trajectories: plan and execute paths in Cartesian space
- Collision-Aware Planning: use MoveIt planning tools for obstacle-aware robotic-arm motion
Move_Group, C++ & Python
MoveIt Move_Group workflows support environment-scene planning and 3D point-cloud inputs. Development can be extended through C++ and Python, with standard planning plugins or custom algorithms integrated into the manipulation stack.
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 education, SLAM, navigation, and standard manipulation development. NVIDIA Jetson configurations provide additional GPU resources for object detection, visual perception, deep-learning workloads, and local AI-model experiments.
LiDAR & Accessory 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) | |
The product selector provides N10P LiDAR, N10P LiDAR + touchscreen, M10P LiDAR, and M10P LiDAR + touchscreen accessory combinations.
Mobile Manipulation & Vision Workflows
The development library combines mobile navigation with robotic-arm perception and manipulation. Examples include RTAB-Map, classic 2D LiDAR SLAM, ORB-SLAM2, YOLO object detection, depth-based following, KCF tracking, AR-tag recognition, RRT exploration, browser camera monitoring, RGB line following, LiDAR following, Nav2 controllers, path recording and playback, and chassis-kinematics examples.
Robotic-arm learning examples include a two-finger adaptive gripper, color-block sorting, color tracking, face tracking, and gesture recognition. The 6-DOF configuration is especially suited to manipulation exercises that benefit from the additional base and gripper rotation axes.
Autonomous-Driving & 3D Reconstruction Workflows
Optional multi-line LiDAR can be integrated for outdoor 3D mapping and environment reconstruction. The development examples also cover automated parking, lane-keeping, steering-decision, and traffic-sign-recognition workflows for educational autonomous-driving research.
Development Resources
Development resources include source code, ROS system images, tutorials, URDF models, MoveIt examples, RViz workflows, mapping and navigation examples, perception and manipulation examples, mobile tools, parameter-tuning utilities, Bluetooth code flashing, and optional 4G / 5G communication workflows.
Voice-related functions in the development library require compatible microphone and speaker hardware. Local AI-model workloads require compatible onboard compute and an appropriate model size.
Which Configuration Should I Choose?
| Learning / Research Goal | Configuration Guidance |
|---|---|
| ROS, SLAM & basic mobile manipulation | 4DOF Arm + Raspberry Pi 5 + N10P |
| Omnidirectional mobile manipulation | 4DOF-Arm - Mecanum Wheel |
| Conventional 4-wheel mobile base | 4DOF-Arm - 4WD |
| More complex arm manipulation | 6DOF-Arm - Mecanum Wheel |
| Computer vision & deep learning | Jetson Orin Nano Super or Orin NX Super |
| Higher-density 2D LiDAR data | M10P LiDAR |
| Onboard local display | Choose a LiDAR + touchscreen accessory option |
Applications
- ROS 1 / ROS 2 robotics education
- Mobile manipulation and robotic-arm control
- MoveIt, RViz, URDF, and motion-planning courses
- SLAM, localization, Nav2, and autonomous exploration
- RGB-D vision, object detection, tracking, and AR-tag research
- Grasping, color sorting, and visual manipulation experiments
- Autonomous-driving algorithm education and 3D reconstruction
- University laboratories and secondary development
Related Products
For a mobile-base-only educational platform, see the LSE550 Multi-Chassis Educational ROS Robot. For standalone 2D ranging, see the M10 / N10 Series 2D TOF LiDAR.
Open-Source ROS & Mobile Manipulation Features
The LSE550A development library combines mobile navigation, robotic-arm manipulation, computer vision, path planning, and secondary development. Functions that depend on optional sensors or voice hardware require the corresponding hardware configuration.
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 dynamic-obstacle navigation 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 training experiments.
6. 3D Reconstruction & Autonomous-Driving Workflows
Optional multi-line LiDAR can be integrated for outdoor 3D mapping and environment-reconstruction research.
Automated Parking
Development examples include automated parking and garage-docking workflows that combine perception and motion-control algorithms.
Lane Keeping
Computer-vision examples support lane detection and vehicle-centering experiments for autonomous-driving education.
Steering Decision Workflows
Lane and traffic-sign perception can be combined with steering-control logic for real-time navigation experiments.
YOLO Traffic Sign Recognition
Traffic-sign-recognition examples include step-by-step model-training workflows using RGB camera input.
7. Depth-Based Visual Following
Uses RGB-D depth information to estimate target distance and bearing for real-time 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 exploration workflow for autonomous 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. ROS TensorFlow Object Detection
TensorFlow examples in ROS cover common object classification and handwritten-digit recognition workflows.
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. Adaptive Flexible Gripper
The two-finger adaptive gripper is designed to accommodate objects with different shapes and surface geometries for grasping experiments.
30. Color Block Sorting
Includes ready-to-use color-sorting routines for hands-on robotic-arm manipulation and pick-and-place learning.
31. Color Tracking
Color-recognition and tracking workflows can be combined with robotic-arm motion for visual manipulation experiments.
32. Real-Time Face Tracking
Face-detection and tracking examples allow the vision system and robot to follow detected facial targets in real time.
33. Dynamic Gesture Recognition
Gesture-recognition examples support static hand signs and dynamic gestures with real-time result display.
Frequently Asked Questions
What is the LSE550A designed for?
The LSE550A is an educational ROS mobile manipulator for mobile-robot navigation, robotic-arm control, MoveIt, RViz, SLAM, computer vision, grasping, object sorting, and AI perception research.
Which model options are available?
The product is available as 4DOF-Arm + Mecanum Wheel, 4DOF-Arm + 4WD, and 6DOF-Arm + Mecanum Wheel.
What is the difference between the 4-DOF and 6-DOF robotic arms?
The 6-DOF version adds a horizontal rotating base and a rotating gripper servo for additional dexterity. The 4-DOF version provides a simpler manipulation platform for standard educational exercises.
Does the 6-DOF version include an additional camera?
Yes. The 6-DOF configuration includes an additional RGB camera to support manipulation tasks when the gripper moves outside the primary depth camera field of view.
Does the LSE550A support MoveIt?
Yes. MoveIt workflows support robotic-arm kinematics, interactive control, Cartesian trajectories, planning scenes, obstacle-aware motion planning, and Move_Group development.
Is a URDF model included?
Yes. The platform includes a 1:1-scale URDF model for RViz visualization, simulation, kinematics, and motion-planning development.
Can I develop robotic-arm software in C++ or Python?
Yes. The MoveIt development workflow supports C++ and Python, and the planning stack can be extended with standard or custom algorithms.
Which onboard computers are available?
Controller options include Raspberry Pi 5 (4 GB or 8 GB), 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.
Can I order the robot with a touchscreen?
Yes. The accessory selector includes both N10P LiDAR + touchscreen and M10P LiDAR + touchscreen configurations.
What SLAM and navigation resources are included?
Development resources include RTAB-Map, Gmapping, Cartographer, slam_toolbox, ORB-SLAM2, RRT exploration, Nav2, TEB, DWA, DWB, MPPI, RPP, path recording and playback, and waypoint navigation examples.
What robotic-arm examples are included?
Examples include adaptive gripping, color-block sorting, color tracking, robotic-arm visual interaction, face tracking, gesture recognition, and MoveIt-based motion planning.
Does the platform support autonomous-driving research?
Yes. Development examples include optional multi-line-LiDAR 3D reconstruction, automated parking, lane keeping, steering-decision workflows, and traffic-sign recognition.
Do voice features require additional hardware?
Yes. Sound-source localization, voice-controlled navigation, 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, MoveIt examples, mapping and navigation examples, perception and manipulation examples, mobile tools, parameter-tuning utilities, and secondary-development resources.
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