Autonomous Kit
The Autonomous Kit is an all-in-one robotics platform for learning, building, and experimenting with intelligent autonomous systems. Designed for both education and research, it brings advanced capabilities like navigation, obstacle avoidance, and real-time sensor fusion into an easy-to-use and modular system.
Who Is It For?
Educators & Students: Ideal for classroom robotics, hands-on STEM education, and project-based learning.
University Labs & Researchers: Supports complex robotics experiments, SLAM, AI, and ROS2 integration.
Makers & Developers: A flexible platform for building autonomous robots and testing advanced algorithms.
Whether you're starting out or pushing the limits of robotic intelligence, the Autonomous Kit adapts to your needs.
What’s Inside the Box?
Everything you need to start building right away:
2x SMD RED Smart Brushed Motor Driver with Speed, Position and Current Control Modes
2x 12V Brushed DC Motor with built-in encoder
1x USB Gateway Module OR 1x Arduino Gateway Module
1x IMU Module
1x Ultrasonic Distance Sensor Module
Learn by Doing
The Autonomous Kit empowers you to:
Program robots using Python or visual Blockly code
Build real-time obstacle-avoiding systems
Visualize sensor data and understand sensor fusion
Design SLAM-based mapping solutions
Prototype custom algorithms for decision-making and mobility
Hands-on learning meets real-world robotics.
Key Features
Fully integrated sensor fusion system (Ultrasonic, IMU, LIDAR)
Wireless (Wi-Fi/Bluetooth) and USB communication
Cross-platform interfaces: Python, GUI, Blockly, Mobile App
Supports both ESP32 and Raspberry Pi
ROS2-ready for advanced robotics development
Modular and open-source hardware/software
Software & Control Options
Blockly UI: Drag-and-drop programming for beginners
Python GUI: Desktop interface with real-time motor and sensor visualization
Python Scripting API: Full control over sensors and actuators
Flutter Mobile App (optional): Control via smartphone over Wi-Fi or Bluetooth
ROS2 Integration: Ideal for SLAM, navigation, and research applications (Raspberry Pi only)
Learning & Experimentation Topics
PID motor control algorithms
Obstacle detection and avoidance
Sensor fusion techniques (IMU + Ultrasonic + LIDAR)
SLAM (Simultaneous Localization and Mapping)
Autonomous navigation and path planning
AI-based robot behavior (vision, logic, etc.)
Example Project: SLAM Navigation Robot
Overview: Build a self-driving robot that maps its surroundings using LIDAR and navigates autonomously using real-time obstacle avoidance and path planning.
Hardware Used:
2× SMD Red Motor Drivers
Ultrasonic Sensor
IMU Sensor
360° LIDAR
Raspberry Pi (recommended) or ESP32
Software Stack:
Python SDK for control and decision logic
GUI for live visualization
ROS2 for SLAM and navigation stack integration
Highlights:
LIDAR-based mapping
Dynamic obstacle detection
Real-time path correction using sensor feedback
Expandable with a camera for visual SLAM or object tracking
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