Rare Find Jetacker Ros Education Robot Car With Ackerman Structure Support Slam Mapping Navigation Learning (standard Kit/jetson Orin Nano 8gb/a1 Lidar) Bundle [jYGCoBJe]

Advanced Learning Platform for Robotics

The JetAcker ROS Education Robot Car is an innovative platform designed for both learning and practical applications in robotics. Equipped with an Ackermann steering structure, this robot car is ideal for understanding and validating steering-based robots. Its robust features make it a versatile tool for educational purposes, research, and development.

Powerful Hardware and Software Integration

The JetAcker is powered by the NVIDIA Jetson Nano, a compact yet powerful computer module that supports ROS, deep learning, MediaPipe, YOLO, and TensorRT. This allows for advanced 3D vision tasks, making it suitable for autonomous driving, somatosensory interaction, and KCF target tracking. The robot car is also equipped with a 3D depth camera and Lidar, enabling remote control, 2D mapping, TEB path planning, and dynamic obstacle avoidance. Here are some of its key features:

• Aluminum Alloy Frame: Durable and lightweight construction for enhanced stability and performance.
• CNC Steering: High-precision CNC steering system for accurate and reliable control.
• 100mm Wheels: Large diameter wheels for better traction and maneuverability.
• Hall Encoder Motors: Precise motor control for consistent performance.
• 6000mAh Battery: Long-lasting battery life for extended operation.

Comprehensive Control and Navigation

The JetAcker offers multiple control options, including the WonderAi app for iOS and Android, wireless handle, ROS, and keyboard. This versatility allows users to interact with the robot in various ways, depending on their preferences and requirements. Additionally, the robot car is equipped with a 240° high-performance pan-tilt, expanding its exploration range and capabilities. With its robust Lidar and 3D vision mapping, the JetAcker can navigate complex environments, perform dynamic obstacle avoidance, and support SLAM mapping and navigation. Here are some practical uses:

• SLAM Mapping and Navigation: Learn and validate various robotic SLAM algorithms with support for Gmapping, HectorKarto, and Cartographer.
• 3D Vision Mapping: Utilize RTAB-VSLAM and ORBSLAM2+ORBSLAM3 for 3D mapping and navigation in a 3D environment.
• Deep Learning and Autonomous Driving: Implement autonomous driving functions, including road sign detection, lane keeping, and automatic parking.
• AI Vision Interaction: Engage with the robot through KCF target tracking, line following, color/tag recognition and tracking, and YOLO object recognition.