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LSR550A Mobile Manipulation Robot Platform with Robotic Arm
- 100% Open-Source Code
- Professional Technical Support
- Comprehensive Development Resources
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The LSR550A is an open-source mobile manipulation robot platform that combines an autonomous mobile base, robotic arm, perception sensors, and ROS development resources in one research system. Available with Mecanum-wheel or 4WD chassis configurations, it is designed for mobile manipulation, autonomous navigation, visual perception, object handling, human-robot interaction, and robotics research.
- Mobile manipulation platform integrating a robotic arm with an autonomous mobile base
- Mecanum-wheel chassis for omnidirectional motion or 4WD chassis for higher-speed mobile operation
- Rated chassis payload: 15 kg for Mecanum configuration and 12 kg for 4WD configuration
- Maximum speed: 1.2 m/s for Mecanum and 2.7 m/s for 4WD
- ROS 1 / ROS 2 development resources for SLAM, navigation, perception, tracking, and path planning
- Raspberry Pi 5, Jetson Nano, Jetson Orin Nano Super, and Jetson Orin NX Super controller options
- N10P or M10P TOF LiDAR with RGB-D depth camera on configured ROS packages
- Adaptive two-finger flexible gripper for manipulation research
- Optional touchscreen and smart-speaker accessories for interaction and voice-oriented applications
Product Overview
LSR550A is intended for research projects that require both mobility and manipulation. Instead of treating the mobile chassis and robotic arm as separate systems, the platform provides a common ROS-based development environment for navigation, perception, target interaction, mobile manipulation, and secondary development.
The platform supports two chassis types. The Mecanum-wheel version provides omnidirectional mobility for indoor laboratories and constrained spaces. The 4WD version uses larger off-road tires and reaches a higher maximum speed, making it suitable for projects that prioritize forward mobility and larger working areas.
Depending on the selected configuration, the robot can be equipped with Raspberry Pi 5 or NVIDIA Jetson computing, N10P or M10P TOF LiDAR, an RGB-D depth camera, a touchscreen, and a smart speaker. The Basic Package is intended for users who want the mobile-manipulation hardware without the ROS computer, LiDAR, and depth-camera package.
Chassis Configuration Comparison
| Parameter | Mecanum Wheel | 4WD |
|---|---|---|
| Drive Type | Omnidirectional Mecanum drive | Four-wheel drive |
| Rated Chassis Payload | 15 kg | 12 kg |
| Maximum Speed | 1.2 m/s | 2.7 m/s |
| Net Weight | 12.6 kg | 13.3 kg |
| Default Dimensions | 460 × 533 × 516 mm | 486 × 523 × 542 mm |
| Drive Wheels | 100 mm metal Mecanum wheels | 152 mm off-road tires |
| Suspension | Coaxial rocker suspension | Coaxial rocker suspension |
| Motor | MD36L 60 W brushed DC motor, 1:27 reduction | MD36L 60 W brushed DC motor, 1:27 reduction |
| Encoder | 500-line GMR AB-phase encoder | 500-line GMR AB-phase encoder |
| Endurance | Approx. 6.5 h no-load / 5.5 h with 3 kg load | |
Mobile Manipulation & Robotic Arm
The integrated robotic arm extends the LSR550A from autonomous navigation into mobile manipulation, visual grasping, object handling, and pick-and-place research. The arm uses a parallel-link mechanical structure that reduces upper-arm load and installation space, together with integrated drive-control stepper motors and an adaptive two-finger flexible gripper.
| Robotic Arm Parameter | Specification |
|---|---|
| Arm Structure | Parallel-link stepper-motor robotic arm |
| Arm Weight | Approx. 3 kg |
| Grasping Payload | Up to 600 g |
| Joint Motor | MS42DDCR integrated drive-control stepper motor |
| Servo | S20F high-torque digital servo |
| Arm Control | CAN / serial control |
| End Effector | Adaptive two-finger flexible gripper |
| Vision Camera | C70 wide-FOV RGB camera, 720P, manual focus |
MS42DDCR Integrated Drive-Control Stepper Motor
| Configuration | Motor + driver + gearbox |
| Reduction Ratio | 1:13.8 |
| Rated Torque | 1180 mN·m at 21 rpm |
| Stall Torque | 3200 mN·m |
| No-Load Speed | 29 rpm |
| Weight | 0.53 kg |
| Motor Type | 2-phase hybrid stepper motor |
| Step Angle | 0.129° |
| Encoder | 500-line encoder with internal power supply |
| Control Inputs | CAN / serial / NPN / host computer |
| Drive Supply Voltage | 10–26 V |
| Motor Current | 1.68 A |
| Motor Resistance | 1.65 Ω |
| Motor Inductance | 3.2 mH |
| Rotor Inertia | 9.522 × 10-4 kg·m² |
| Protection | Input reverse-polarity protection |
C70 RGB Camera for Visual Grasping
| Resolution | 720P |
| Sensor | CMOS |
| Field of View | H85° / V47° |
| Frame Rate | 30 fps |
| Dimensions | 47 × 38 × 28.5 mm |
| Weight | 50 g |
| Supported Systems | Windows XP / Vista / 7 / 8 / 10 and Ubuntu |
ROS examples include color-block visual recognition and grasping workflows using the arm-mounted camera and gripper.
Onboard Computer Options
| Controller | Raspberry Pi 5 | Jetson Nano | Orin Nano Super | Orin NX Super |
|---|---|---|---|---|
| Memory | 8 GB | 4 GB LPDDR4 | 8 GB LPDDR5 | 16 GB LPDDR5 |
| Storage | 64 GB MicroSD | 64 GB MicroSD | 256 GB SSD | 256 GB SSD |
| System Image | ROS 1 Melodic / ROS 2 Humble | ROS 1 Melodic / ROS 2 Galactic | ROS 1 Noetic / ROS 2 Humble | ROS 1 Noetic / ROS 2 Humble |
LiDAR & RGB-D Perception
| 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 |
| Ranging Principle | TOF | TOF |
| Scanning Range | 360° | 360° |
Configured ROS packages also include an RGB-D depth camera for visual perception, depth-based following, object tracking, visual mapping, and manipulation-related perception experiments.
Open-Source ROS & AI Development
The development library covers RTAB-Map 3D mapping, 2D LiDAR SLAM, ORB-SLAM2, ROS Qt tools, YOLO object detection, TensorFlow object detection, depth-based following, KCF tracking, AR-tag recognition, RRT autonomous exploration, web camera monitoring, RGB line following, LiDAR following, LiDAR angle masking, voice-oriented interaction workflows, TEB / DWA planning, Nav2 DWB / MPPI / RPP controller examples, coverage planning, chassis kinematics, path recording and playback, URDF models, mobile apps, and optional 4G / 5G remote communication.
Some functions require the corresponding hardware. For example, voice localization and voice-controlled navigation require an appropriate microphone / speaker configuration, while vision-based functions require the selected camera and sufficient onboard computing resources.
Which Configuration Should I Choose?
Mecanum Wheel
Choose the Mecanum configuration when omnidirectional movement, lateral positioning, and maneuverability in indoor laboratories are important to the manipulation task.
4WD
Choose the 4WD configuration when higher travel speed, larger tires, and conventional four-wheel mobile operation better match the research environment.
Controller Selection
Raspberry Pi 5 is suitable for standard ROS development and navigation workloads. NVIDIA Jetson options provide GPU resources for computer vision and AI workloads, with Orin Nano Super and Orin NX Super offering the highest AI compute among these options.
Applications
Mobile Manipulation & Pick-and-Place Research
Combine autonomous navigation, visual perception, target localization, and robotic-arm control for mobile manipulation, object transport, laboratory automation, and service-robot research.
SLAM, Navigation & Semantic Robotics
Use the platform for LiDAR SLAM, visual mapping, Nav2 navigation, autonomous exploration, semantic task execution, indoor service robotics, and research that links higher-level environment information with robot motion.
Computer Vision & Human-Robot Interaction
RGB-D vision, object detection, target tracking, AR-tag detection, optional voice interaction, and GPU computing options support computer-vision and human-robot interaction experiments.
Need Help Selecting?
If you need a mobile platform without an integrated manipulator, compare the LSR550 4WD research robot and the LSR550 OMNI research robot. For a compact indoor differential platform, see the LSR100 service robot platform, or browse the Research Robot Platforms collection.
Key Features & Open-Source Development Resources
The LSR550A includes a broad ROS, navigation, perception, interaction and mobile-manipulation development library. Individual functions depend on the selected onboard computer, sensors, accessories and optional communication or voice hardware.
Robotic Arm Hardware
Parallel-Link Mechanical Structure
The arm uses a parallel-link structure that reduces upper-arm load and saves installation space. The complete arm weighs approximately 3 kg and supports grasping payloads up to 600 g.
Integrated Drive-Control Stepper Joints
MS42DDCR integrated drive-control stepper motors combine the motor, driver and gearbox in one module. The joint motor uses a 1:13.8 reduction ratio, 500-line encoder, CAN / serial / NPN / host-control inputs, and a 10–26 V drive supply.
C70 Wide-FOV RGB Camera
The arm-mounted C70 RGB camera provides 720P video, a CMOS sensor, H85° / V47° field of view and 30 fps output for visual recognition and grasping experiments.
ROS Visual Recognition & Grasping
ROS examples include color-block visual recognition and grasping workflows that combine the camera, robotic arm and adaptive flexible gripper.
1. RTAB-Map 3D VSLAM & LiDAR Integration
Supports RTAB-Map vision-only mapping and LiDAR–vision fusion mapping 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. Navigation examples cover point-to-point navigation, multi-point waypoints and dynamic obstacle avoidance.
3. ORB-SLAM2 Visual Mapping
Includes ORB-SLAM2 for real-time camera pose estimation and sparse 3D reconstruction. RGB-D mode provides metric-scale depth information for visual mapping experiments.
4. ROS Qt Graphical User Interface
A Qt-based ROS interface provides convenient 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. ROS TensorFlow Object Detection
Includes TensorFlow-based ROS examples for object classification and handwritten-digit recognition experiments.
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 resources for identifying and tracking targets with stable visual features.
9. AR Tag Recognition & Following
Supports 6-DOF AR-tag pose detection for tag-following behaviors 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 tasks.
12. RGB Camera Line Following
Supports RGB-vision line following with LiDAR-assisted obstacle avoidance for line-following research and demonstrations.
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 the appropriate microphone-array hardware, the workflow supports sound-direction estimation for voice-interaction and navigation experiments. Directional precision is 1°.
16. Voice-Activated Summoning
With the corresponding voice hardware, the robot can use microphone-array direction information together with LiDAR distance sensing in voice-driven summoning experiments.
17. Voice-Controlled Navigation
Supports voice-command workflows that associate recognized commands with predefined navigation points on a saved map when the required voice hardware is installed.
18. TTS Interaction
Includes text-to-speech interaction workflows and can be extended with compatible online voice-dialogue services.
19. TEB & DWA Path Planning
Includes tutorial 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 navigation scenarios and robot footprints.
21. Full Coverage Path Planning
Supports generation of coverage paths for user-defined areas, useful for cleaning, inspection and systematic-area traversal research.
22. Chassis Kinematics Analysis
Development resources include kinematic-analysis 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. Comprehensive URDF Models
Includes URDF models for simulation, visualization and robot-software development based on the physical platform geometry.
25. ROS Mobile App for Navigation
A mobile application is provided for supported ROS motion-control, mapping and navigation workflows.
26. Hardware Tuning & Parameter App
Android and iOS tools support real-time parameter tuning, gravity-sensing control and waveform visualization for supported hardware functions.
27. Optional 4G/5G Remote Modules
Optional cellular communication modules can be used for long-range video transmission and remote-control development over supported mobile networks.
28. Adaptive Flexible Gripper
The integrated two-finger flexible gripper is intended for grasping objects with a variety of shapes and surface characteristics, with a grasping payload of up to 600 g for mobile-manipulation and pick-and-place experiments.
Frequently Asked Questions
What is the LSR550A designed for?
LSR550A is designed for mobile manipulation, ROS research, SLAM and navigation, visual perception, object handling, service robotics, human-robot interaction, and secondary development.
What is the difference between the Mecanum and 4WD versions?
The Mecanum version provides omnidirectional motion and has a 15 kg chassis payload and 1.2 m/s maximum speed. The 4WD version uses 152 mm off-road tires and has a 12 kg chassis payload and 2.7 m/s maximum speed.
Does the LSR550A include a robotic arm?
Yes. LSR550A is a mobile robot-arm platform with an adaptive two-finger flexible gripper for manipulation experiments.
What are the main robotic-arm specifications?
The robotic arm uses a parallel-link stepper-motor structure, weighs approximately 3 kg, and has a grasping payload of up to 600 g. It uses MS42DDCR integrated drive-control stepper motors, an S20F high-torque digital servo, CAN / serial control, and an adaptive two-finger flexible gripper.
What camera is used for visual grasping?
The arm uses a C70 wide-FOV RGB camera with 720P resolution, CMOS sensor, H85° / V47° field of view, 30 fps output, manual focus, and a weight of 50 g. ROS examples include color-block visual recognition and grasping.
Which onboard computers can I choose?
Controller options include a Basic Package, Raspberry Pi 5 (8 GB), Jetson Nano (4 GB), Jetson Orin Nano Super (8 GB), and Jetson Orin NX Super (16 GB). The Basic Package does not include the ROS computer, LiDAR, or depth-camera bundle.
Which ROS versions are supported?
Raspberry Pi 5 supports ROS 1 Melodic / ROS 2 Humble, Jetson Nano supports ROS 1 Melodic / ROS 2 Galactic, and Orin Nano Super / Orin NX Super support ROS 1 Noetic / ROS 2 Humble.
What LiDAR options are available?
ROS configurations offer N10P or M10P TOF LiDAR. N10P has a 25 m detection radius and adjustable 6–12 Hz scanning; M10P has a 30 m detection radius and 12 Hz scanning.
Does the robot include a depth camera?
Configured ROS packages include a depth camera together with the selected N10P or M10P LiDAR. The Basic Package does not include the controller, LiDAR, or depth-camera bundle.
What SLAM and navigation resources are included?
Development resources include 2D LiDAR SLAM, RTAB-Map, ORB-SLAM2, Nav2 workflows, RRT autonomous exploration, path recording and playback, full-coverage planning, and DWB / MPPI / RPP controller examples.
Does it support computer vision and object tracking?
Yes. Development resources include YOLO object detection, TensorFlow examples, RGB-D following, KCF tracking, AR-tag recognition, RGB line following, and web-based camera monitoring.
Does the platform support voice interaction?
Voice-oriented development workflows include sound-source localization, voice-controlled navigation and TTS interaction. These functions require the corresponding microphone / speaker hardware, and an optional smart-speaker accessory is available.
Can I add 4G or 5G remote communication?
Yes. Optional 4G / 5G communication modules are supported for cellular video transmission and remote-control development.
What control interfaces are available on the Basic Package?
The Basic Package supports serial communication, CAN bus, a multi-mode smart controller, and mobile-app control.
What development materials are provided?
Development resources include manuals, video tutorials, source code, ROS system images, URDF resources, navigation and perception examples, mobile tools, and secondary-development materials.
Which configuration should I choose for AI and vision workloads?
For standard ROS navigation and educational development, Raspberry Pi 5 can be suitable. For GPU-based computer vision and AI workloads, the NVIDIA Jetson options provide additional compute capability, with Orin Nano Super and Orin NX Super offering the highest AI compute capability among these options.
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