The LanderPi robot car 

A composite ROS educational robot developed by Hiwonder. It supports three motion chassis: the Mecanum wheel, the Ackerman chassis, and the Tank chassis. It is equipped with a Raspberry Pi, high-torque coding motors, and a 6DOF robotic arm. High-performance hardware configurations such as lidar, a 3D depth camera, and an AI voice interaction box enable robot motion control, mapping navigation, path planning, obstacle tracking and avoidance, autonomous driving, 3D input, navigation and manipulation, somatosensory interaction, AI voice interaction, group control training, and other applications.

LanderPi also deploys a large, multimodal AI model to support more advanced embedded 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.

 

SLAM Lidar Mapping Navigation

The LanderPi robot car is equipped with lidar, which can perform SLAM mapping and navigation, and supports path planning, fixed-point navigation, and dynamic obstacle avoidance.

 

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 accurate object recognition, tracking, and grasping.

 

WonderEcho Pro AI Voice Box

The LanderPi Advanced Kit comes with a WonderEcho Pro AI voice box that offers excellent noise reduction and clear audio capture. It supports advanced features such as voice recognition, voice broadcasting, and voice control.

 

Dual controller design for efficient collaboration

 

LIST OF FUNCTIONS 

 

Integration of the large AI model with SLAM mapping and navigation

The Hiwonder LanderPi car combines a large multimodal model to understand user voice commands via a large language model, enabling multipoint navigation. Once it arrives at the designated location, it uses a vision language model to gain a deep understanding of surrounding objects and events. This approach significantly enhances the robot's intelligence, adaptability, and overall user experience, making it better suited to meet real-world needs.

 

 

Semantic comprehension

The LanderPi robot car leverages a large language model to accurately interpret and analyze user voice commands, enabling a deeper understanding of natural language intent.

 

Intelligent navigation

The LanderPi Raspberry Pi robot continuously sends environmental data to its vision language model for in-depth, real-time analysis. It dynamically adjusts its navigation path based on user voice commands, enabling it to autonomously navigate to designated areas and provide intelligent, adaptive routing.

 

Environmental perception

Powered by a vision language model, LanderPi can interpret objects in its environment and understand the spatial arrangement of the environment.

Scene comprehension

With the support of a vision language model, LanderPi can deeply interpret semantic information from its environment, including surrounding objects and events in its field of vision.

 

 

AI applications embedded in large models

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 improving the fluidity and naturalness of human-machine interaction. Combined with computer vision, LanderPi exhibits advanced capabilities in perception, reasoning, and autonomous action, paving the way for more sophisticated embedded AI applications.

 

Voice command

With ChatGPT integration, LanderPi can understand voice commands and perform corresponding actions, enabling intuitive and seamless voice interaction.

 

Autonomous patrol

By using the semantic understanding of a large language model, LanderPi can accurately detect and track lines of different colors in real time while autonomously navigating around obstacles, ensuring smooth and efficient patrolling.

 

Color tracking

LanderPi uses 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, real-time target tracking.

 

Vision tracking

Thanks to the advanced perception capabilities of a vision language model, LanderPi can intelligently identify and lock onto target objects even in complex environments, enabling it to perform real-time tracking with adaptability and accuracy.

 

 

Various control methods

 

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