MentorPi Joins the ROS2 Series: An Ideal Entry-Level Robot Car
Drawing on extensive experience in developing the ROS product line, Hiwonder has introduced an exciting new robot car MentorPi, perfect for beginners.
This desktop ROS2 robot car is designed for educational use, offering a budget-friendly, dual-controller system with optional chassis configurations. It’s a versatile platform that lets users experience the power and flexibility of ROS2 robotics without breaking the bank.
ROS2: Deep Integration, Endless Possibilities ROS2: Deep Integration,
Endless Possibilities
Endless Possibilities
MentorPi is built on the latest ROS2, the most advanced robot operating system, offering high efficiency, scalability, and real-time performance. With its distributed architecture, ROS2 enables robots to coordinate and communicate seamlessly in complex environments, enhancing task execution and system responsiveness.
The ROS2 framework allows MentorPi to thrive in challenging settings. It not only handles basic robot control tasks but also enables multiple functional modules to work simultaneously, improving development experience.
Additionally, MentorPi supports the ROS2 simulation environment, allowing developers to test, debug, and virtually deploy algorithms before using real hardware. For those working on autonomous navigation and exploration, MentorPi offers a robust simulation platform that significantly reduces project development time.
Concerned about Raspberry Pi 5 compatibility with the ROS2 environment? No worries. MentorPi integrates Docker containers, allowing developers to package applications and easily transfer them across machines for seamless deployment. Deeply integrated with ROS2, MentorPi is a versatile solution for education, research, and rapid development, helping users get started quickly and effectively.
Hardware Innovation: Dual-Controller
Architecture for Enhanced Performance Hardware Innovation: Dual-Controller Architecture for Enhanced Performance
Architecture for Enhanced Performance Hardware Innovation: Dual-Controller Architecture for Enhanced Performance
MentorPi features a Raspberry Pi 5 as its main controller, serving as the "brain" that manages AI vision and strategy deployment. This powerful setup enables the robot to run mainstream deep learning frameworks and validate advanced AI functions. Complementing this is Hiwonder's self-developed RRC Lite expansion board, acting as the "sub-brain" responsible for motion control and sensor data processing. The collaboration between these two components results in a significant performance boost.
The unique dual-controller architecture alleviates the burden on the upper computer by distributing tasks, improving processing efficiency and response speed. This design allows the lower computer to focus primarily on basic functions, streamlining system deployment and enhancing MentorPi's capabilities in vision applications, robot control, and machine learning.
This hardware innovation maximizes MentorPi’s performance and expands its range of applications.
Functionality and Versatility:
Diverse Application Scenarios
Diverse Application Scenarios
MentorPi is designed for a wide range of applications. It integrates seamlessly with various hardware and operates efficiently in different environments, unlocking endless possibilities. Its diverse functionality broadens the robot’s capabilities, providing developers with greater opportunities for innovation.
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Two Chassis Options for Flexible AdaptationMentorPi offers two chassis options: the 360-degree Mecanum-wheel chassis for omnidirectional movement and the Ackermann chassis for smooth, flexible steering. Users can select the chassis that best suits their development needs, enabling seamless adaptation to various environments.
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3D Depth Technology for Advanced Vision ExplorationMentorPi is equipped with a high-performance binocular structured-light depth camera. In RGB-D mode, the camera captures 3D depth information from the environment and target objects.
This cutting-edge vision technology provides clear RGB images, enabling MentorPi to perform RTAB-VSLAM for 3D mapping and navigation, while also supporting global localization.
As application scenarios expand, the 3D depth camera, paired with its API, delivers stable, high-quality depth maps for MentorPi. Additionally, it can capture RGB images, point cloud data, and 3D depth information, allowing MentorPi to fully perceive and interact with its environment.
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Lidar Obstacle Detection and Autonomous Path PlanningMentorPi comes equipped with a high-performance Lidar integrated with a self-developed, high-precision encoder and IMU accelerometer and gyroscope data. This allows MentorPi to achieve precise mapping and navigation while supporting path planning, fixed-point navigation, and obstacle avoidance.
In real-world scenarios, MentorPi scans its surroundings using Lidar. When an obstacle is detected, it sounds an alert and tracks the target for efficient obstacle avoidance.
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MediaPipe Recognition for Advanced Application SimulationsBy training its deep learning model library and optimizing the AI algorithms, MentorPi can perform advanced recognition tasks such as human body detection, fingertip recognition, facial detection, and 3D sensing. These capabilities enable flexible AI interactions and open the door to sophisticated application simulations.
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Deep Integration of ROS2 for Autonomous Driving
MentorPi is equipped with the PyTorch deep learning framework and the OpenCV image processing library, empowering users to develop AI-based autonomous driving projects. For those working on autonomous driving technology, these tools offer a solid foundation for innovation.
Hiwonder has made the robot's control driver files open source and provides RVIZ use cases, allowing users to customize application scenarios and explore limitless possibilities.