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Hiwonder
Hiwonder LanderPi Raspberry Pi Robot Car with AI Vision Robotic Arm, Support ROS2, Large AI Model (ChatGPT), SLAM Mapping/Navigation, AI Voice Interaction, Intelligent Sorting
Product SKU: 21031613
【Raspberry Pi 5 & ROS2 Robot Car】LanderPi is powered by Raspberry Pi 5, compatible with ROS2, and programmed in Python, making it an ideal platform for AI robot development.
【Multiple Chassis Configurations】 Supports Mecanum-wheel, Ackermann chassis, and tank chassis, allowing flexibility for various applications and meeting diverse user needs.
【High-Performance Hardware】Equipped with DC gear encoder motors, TOF lidar, 3D depth camera, 6DOF Robotic Arm, and other advanced components to ensure optimal performance and efficiency.
【AI Advanced AI Capabilities】Supports SLAM mapping, path planning, multi-robot coordination, vision recognition, target tracking, and more, covering a wide range of AI applications.
【Autonomous Driving with Deep Learning】Utilizes the YOLOv8 model training to enable road sign and traffic light recognition, along with other autonomous driving features, helping users explore and develop autonomous driving technologies.
【Empowered by Large AI Model, Human-Robot Interaction Redefined】LanderPi deploys multimodal models with ChatGPT at its core, integrating 3D vision robotic arm and AI voice interaction box. This synergy enhances its perception, reasoning, and actuation capabilities, enabling advanced embodied AI applications and delivering natural, context-aware human-robot interaction.
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Product Description
LanderPi is a composite ROS educational robot developed by Hiwonder. It supports three motion chassis: Mecanum wheel, Ackerman, and Tank chassis. It is equipped with Raspberry Pi, large-torque encoding motors, and 6DOF robotic arm. High-performance hardware configurations such as lidar, 3D depth camera, and AI voice interaction box can realize robot motion control, mapping navigation, path planning, tracking and obstacle avoidance, autonomous driving, 3D grabbing, navigation and handling, somatosensory Interaction, AI voice interaction, group control formation, and other applications.
LanderPi also deploys a Multimodal Large AI Model to support more advanced embodied AI applications. To help you unlock its full potential, open-source code and learning resources are provided to inspire and support your AI projects.
LanderPi also deploys a Multimodal Large AI Model to support more advanced embodied AI applications. To help you unlock its full potential, open-source code and learning resources are provided to inspire and support your AI projects.
6DOF Robot Arm, Intelligent Bus Servo
Equipped with a 6DOF robot arm and high-torque bus servos, LanderPi achieves superior performance and longer operating endurance.
Lidar SLAM Mapping Navigation
LanderPi robot car is equipped with lidar, which can realize SLAM mapping and navigation, and supports path planning, fixed-point navigation and dynamic obstacle avoldance.
Depth Vision First-Person View
With a 3D depth camera mounted at the end of its 6DOF robotic arm, LanderPi provides first-person visual intelligence for precise object recognition, tracking, and grasping.
WonderEcho Pro AI Voice Box
LanderPi Advanced Kit comes with a WonderEcho Pro AI voice box that delivers excellent noise reduction and clear audio capture. It supports advanced features such as speech recognition, voice broadcast, and voice control.
Dual-Controller Design for Efficient Collaboration
Function List
Integration of Large AI Model with SLAM Mapping and Navigation
LanderPi combines multimodal large model to understand user voice commands via a large language model, enabling multi-point navigation. Once it arrives at the designated location, it uses a vision language model to gain a deep understanding of the surrounding objects and events. This approach greatly enhances the robot's intelligence, adaptability, and overall user experience, making it better suited to meet real-world needs.
Semantic Understanding
LanderPi leverages a large language model to accurately interpret and analyze user voice commands, enabling a deeper understanding of natural language intent.
Intelligent Navigation
LanderPi continuously sends environmental data to the vision language model for real-time in-depth analysis. It dynamically adjusts its navigation path based on user voice commands, allowing it to autonomously navigate to designated areas and deliver intelligent, adaptive routing.
Environmental Perception
Powered by a vision language model, LanderPi can interpret objects in its surroundings and understand the spatial layout of the environment.
Scene Understanding
With the support of a vision language model, LanderPi can deeply interpret the semantic information of its environment, including surrounding objects and events within its field of view.
Large Model Embodied AI Applications
LanderPi is equipped with a high-performance AI voice interaction module. Unlike conventional AI systems that operate on unidirectional command-response mechanisms, LanderPi leverages ChatGPT to enable a cognitive transition from semantic understanding to physical execution, significantly enhancing the fluidity and naturalness of human-machine interaction. Combined with machine vision, LanderPi exhibits advanced capabilities in perception, reasoning, and autonomous action—paving the way for more sophisticated embodied AI applications.
Voice Control
With ChatGPT integration, LanderPi can comprehend spoken commands and carry out corresponding actions, enabling intuitive and seamless voice-controlled interaction.
Autonomous Patrolling
Utilizing semantic understanding from a large language model, LanderPi can accurately detect and track lines of various colors in real time while autonomously navigating obstacles, ensuring smooth and efficient patrolling.
Color Tracking
LanderPi utilizes vision language model analysis to detect and lock onto any object within its field of view. With the integration of a PID algorithm, it achieves precise and real-time target tracking.
Vision Tracking
With the advanced perception capabilities of a vision language model, LanderPi can intelligently identify and lock onto target objects even in complex environments, allowing it to perform real-time tracking with adaptability and precision.
Why Choose US
LanderPi is equipped with a professional-grade Lidar that delivers true 360° panoramic scanning, enabling full-scene perception without blind spots. Powered by the fusion of 3D vision and IMU technology, it intelligently plans the shortest and safest routes with remarkable precision. Even in rapidly changing environments, LanderPi adapts instantly, ensuring mapping and navigation remain efficient, smooth, and highly accurate at all times.From smart home cleaning robots to advanced industrial automation, this single-Lidar solution provides unmatched reliability and performance across diverse scenarios.
Did You Know? Adding more Lidars doesn't guarantee better results. Two low-cost Lidars stitched together often suffer from scan overlaps, noisy data, and synchronization errors—leading to blurry maps and delayed obstacle avoidance. That's why a single high-end Lidar consistently outperforms multiple cheap alternatives.
Did You Know? Adding more Lidars doesn't guarantee better results. Two low-cost Lidars stitched together often suffer from scan overlaps, noisy data, and synchronization errors—leading to blurry maps and delayed obstacle avoidance. That's why a single high-end Lidar consistently outperforms multiple cheap alternatives.
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ROS Robot Operating System
Global Popular Robotic Communication Framework
ROS is an open-source meta operating system for robots. It provides some basic services, such as hardware abstraction, low-level device control, implementation of commonly used functionality, message-passing between processes, and package management. And it also offers the tools and library functions needed to obtain, compile, write, and run code across computers. It aims at providing code reuse support for robotics research and development.
URDF Simulation
LanderPi is developed on the ROS framework and supports URDF simulation. Through the Rviz visualization tool, mapping and navigation results can be observed, enabling convenient debugging and algorithm optimization.
| Aurora930 Pro Specifications | |||||
| Module parameter | Size | 76.5 × 20.7 × 21.8 mm | Imaging performance | Depth data format | 16-bit Raw |
| Baseline | 40mm | Depth resolution/Frame rate | 640×400 @12fps (FOV: 74°×51°) | ||
| Interface | USB 2.0 Wafer connector | RGB data format | NV12 | ||
| Depth accuracy | ±8mm @1m | RGB resolution /Frame rate | 640×400 @12fps (FOV: 74°×51°) | ||
| Working distance | 15–300 cm | IR data format | 8-bit Raw | ||
| Operating temperature | -10℃ to 55℃ | IR resolution /Frame rate | 640×400 @12fps (FOV: 74°×51°) | ||
| Operating humidity | 0% to 95% RH (non-condensing) | Firmware capabilities | Firmware upgrade | Supports USB OTA Update | |
| Operating illuminance | 3–80,000 Lux | Hot start delay | <300ms | ||
| Power supply | 5V±10%, 1.5A | System compatibility | OS compatibility | Linux / ARMv8 / ROS / Windows | |
| Power consumption | Average <1.6W | ||||
| Safety rating | Class 1 Laser safety | ||||
| Aurora930 Pro Specifications | ||
| Module parameter | Size | 76.5 × 20.7 × 21.8 mm |
| Baseline | 40mm | |
| Interface | USB 2.0 Wafer connector | |
| Depth accuracy | ±8mm @1m | |
| Working distance | 15–300 cm | |
| Operating temperature | -10℃ to 55℃ | |
| Operating humidity | 0% to 95% RH (non-condensing) | |
| Operating illuminance | 3–80,000 Lux | |
| Power supply | 5V±10%, 1.5A | |
| Power consumption | Average <1.6W | |
| Safety rating | Class 1 Laser safety | |
| Imaging performance | Depth data format | 16-bit Raw |
| Depth resolution/Frame rate | 640×400 @12fps (FOV: 74°×51°) | |
| RGB data format | NV12 | |
| RGB resolution /Frame rate | 640×400 @12fps (FOV: 74°×51°) | |
| IR data format | 8-bit Raw | |
| IR resolution /Frame rate | 640×400 @12fps (FOV: 74°×51°) | |
| Firmware capabilities | Firmware upgrade | Supports USB OTA Update |
| Hot start delay | <300ms | |
| System compatibility | OS compatibility | Linux / ARMv8 / ROS / Windows |
Wrapped Rear Tail Shell
It can effectively protect the PCB circuit and magnetic ring at the end of the motor from external influences, effectively improving the safety and service life of the motor.
Permanent Magnet Brushed Motor
The permanent magnet DC motor has fast starting response speed, large starting torque and smooth speed change.
High-precision Magnetic Encoder
The motor is equipped with a high-precision magnetic encoder, has strong horsepower, high precision, and strong anti-interference ability.
Durable Metal Gear
The motor incorporates a full metal gear and metal output shaft, reducing power consumption and extending the motor's service life.
Hall Encoder Geared Motor
The Hall speed measurement code disc is a speed measurement module that utilizes Hall sensor encoders. Equipped with a strong magnetic disc, it generates AB phase output pulse signals, enabling the detection of motor rotation direction and speed.
7.4V 2200mAh 10C LiPo Battery
The LiPo battery features high-quality 18650 cells and a built-in protection board that safeguards against damage from overcharging, overcurrent, overdischarge, and short circuits. It provides a long service life, with over 300 charge cycles.
Specification Parameters
| Machine model | Mecanum chassis version (3D depth camera) | Ackerman chassis version (3D depth camera) | Tank chassis version (3D depth camera) |
| Chassis type | Mecanum chassis | Ackerman chassis | Tank chassis |
| Size | 212*174*441mm | 213×160×448mm | 278×195×458mm |
| Weight | 1.79kg | 1.73kg | 2.18kg |
| Motor | 310 Metal Geared DC Motor (for Mecanum Wheel & Ackermann Chassis) MC520 Metal Geared DC Motor (for Tracked Chassis) | ||
| Encoder | AB-phase High-precision Quadrature Encoder | ||
| Body material | Full Metal Aluminum Alloy Chassis with Anodized Surface Treatment | ||
| ROS controller | RRCLite Robot Controller + Raspberry Pi 5 Mainboard | ||
| Camera | Aurora930 Pro 3D Depth Camera | ||
| LiDAR | Aoride MS200 LiDAR |
| Audio / pickup | WonderEchoPro AI Voice Interaction Box |
| Battery | 7.4V 2200mAh 10C Lithium Battery (with protection board), 60min runtime (varies by usage) |
| Operating system | Raspberry Pi OS + Ubuntu 22.04 LTS + ROS2 Humble (Docker) |
| Software | iOS / Android Mobile App |
| Communication | WiFi / Ethernet |
| Programming tools | Python / C / C++ / JavaScript |
| Storage | 64GB TF Card |
| Servo models | HX-06L Smart Bus Servo, LD-1501MG Digital Servo (Ackermann Chassis Version) |
| Supporting materials | Development Manual, Video Tutorials, ROS Source Code, System Image, Supporting Software |
| Mecanum chassis version (3D depth camera) | Size: 212*174*441mm Weight: 1.79kg |
| Ackerman chassis version (3D depth camera) | Size: 213×160×448mm Weight: 1.73kg |
| Tank chassis version (3D depth camera) | Size: 278×195×458mm Weight: 2.18kg |
| Motor | 310 Metal Geared DC Motor (for Mecanum Wheel & Ackermann Chassis) MC520 Metal Geared DC Motor (for Tracked Chassis) |
| Encoder | AB-phase High-precision Quadrature Encoder |
| Body material | Full Metal Aluminum Alloy Chassis with Anodized Surface Treatment |
| ROS controller | RRCLite Robot Controller + Raspberry Pi 5 Mainboard |
| Camera | Aurora930 Pro 3D Depth Camera |
| LiDAR | Aoride MS200 LiDAR |
| Audio / pickup | WonderEchoPro AI Voice Interaction Box |
| Battery | 7.4V 2200mAh 10C Lithium Battery (with protection board), 60min runtime (varies by usage) |
| Operating system | Raspberry Pi OS + Ubuntu 22.04 LTS + ROS2 Humble (Docker) |
| Software | iOS / Android Mobile App |
| Communication | WiFi / Ethernet |
| Programming tools | Python / C / C++ / JavaScript |
| Storage | 64GB TF Card |
| Servo models | HX-06L Smart Bus Servo, LD-1501MG Digital Servo (Ackermann Chassis Version) |
| Supporting materials | Development Manual, Video Tutorials, ROS Source Code, System Image, Supporting Software |
ArmPi Ultra Starter Packing List
ArmPi Ultra Standard Packing List
ArmPi Ultra Advanced Packing List
| Item | Specification |
| Mecanum chassis version (3D depth camera) | Size: 212*174*441mm; Weight: 1.79kg |
| Ackerman chassis version (3D depth camera) | Size: 213×160×448mm; Weight: 1.73kg |
| Tank chassis version (3D depth camera) | Size: 278×195×458mm; Weight: 2.18kg |
| Motor | 310 Metal Geared DC Motor (for Mecanum Wheel & Ackermann Chassis) MC520 Metal Geared DC Motor (for Tracked Chassis) |
| Encoder | AB-phase High-precision Quadrature Encoder |
| Body material | Full Metal Aluminum Alloy Chassis with Anodized Surface Treatment |
| ROS controller | RRCLite Robot Controller + Raspberry Pi 5 Mainboard |
| Camera | Aurora930 Pro 3D Depth Camera |
| LiDAR | Aoride MS200 LiDAR |
| Audio / pickup | WonderEchoPro AI Voice Interaction Box |
| Battery | 7.4V 2200mAh 10C Lithium Battery (with protection board), 60min runtime (varies by usage) |
| Operating system | Raspberry Pi OS + Ubuntu 22.04 LTS + ROS2 Humble (Docker) |
| Software | iOS / Android Mobile App |
| Communication | WiFi / Ethernet |
| Programming tools | Python / C / C++ / JavaScript |
| Storage | 64GB TF Card |
| Servo models | HX-06L Smart Bus Servo, LD-1501MG Digital Servo (Ackermann Chassis Version) |
| Supporting materials | Development Manual, Video Tutorials, ROS Source Code, System Image, Supporting Software |
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