What Are Dual-Brain Control Systems Commonly Used For in Robotics?
In traditional single-controller robotic systems, the controller acts as the brain of the robot, responsible for both decision-making and motion control. However, this often leads to shallow thinking and clunky execution. Here comes the 'Dual-Brain Architecture'—the ultimate solution to this challenge. It separates decision-making and control into two distinct units: a "main brain" for processing decisions, and a "secondary brain" for precise control, working together seamlessly for efficient performance.
Take Hiwonder's MentorPi, which uses the powerful combination of the "Raspberry Pi 5 + STM32 controller," as an example. Let’s discover why this is the top hardware solution for robotics!
The Core Structure: A Precision Split Between Decision-Making and Execution
What exactly is Dual-Brain Architecture? In simple terms, it separates decision-making from execution, using two independent controllers that work together to carry out complex, coordinated tasks—delivering a performance boost where 1+1 > 2.
The Main Brain: The Raspberry Pi 5 controller functions as the upper computer, primarily responsible for handling complex tasks such as image recognition, deep neural network inference, and SLAM-based mapping and navigation—requiring high computational power.
The Secondary Brain: The STM32 controller acts as the secondary unit, taking charge of real-time tasks like high-frequency PID control, motor closed-loop regulation, servo positioning, and IMU sensor data collection—ensuring precise, stable control and execution for tasks that demand real-time responsiveness.
MentorPi: Real-World Application of Dual-Brain Collaboration
Let’s better understand the advantages of this architecture through a practical scenario:
When MentorPi performs SLAM mapping and navigation, the Raspberry Pi "main brain" uses radar data to create a map of the surrounding environment and plots a clear, unobstructed path for navigation.
When MentorPi performs SLAM mapping and navigation, the Raspberry Pi "main brain" uses radar data to create a map of the surrounding environment and plots a clear, unobstructed path for navigation.
Then it sends a simple "follow the path" command to the STM32 "secondary brain". The STM32 controller responds in milliseconds, driving the motors along the planned route. Meanwhile, it continuously adjusts the robot's direction and posture in motion, maintaining smooth and stable movement throughout.
💡For Resources: MentorPi tutorials here, and open-source code on Hiwonder GitHub.
Dual-Brain Architecture: Why It’s the Future of Robotic Control Systems
Compared to the response delays and resource contention of traditional single-controller systems, the dual-brain architecture offers three key advantages, making it the ideal hardware solution for advanced mobile robots.
- Clear Task Division for Maximized Performance: The dual-brain architecture achieves task segmentation, allowing the upper computer to focus on AI and vision processing, while the secondary controller handles real-time control and data collection, overcoming the performance limitations of a single-controller system.
- Enhanced Stability and Real-Time Performance: During robotic movement, tasks like motor control and IMU data processing demand exceptional real-time performance. The STM32 controller can react within milliseconds, performing PID control and motor driving smoothly and stably!
- Flexible Expansion for More Efficient Development: The STM32 open-source controller not only comes with an onboard IMU sensor, but also supports four-channel encoder motors, PWM servos, and bus servos. It provides communication interfaces like USB and serial ports, enabling quick integration of devices such as Lidar and depth cameras. Additionally, it supplies stable 5.1V/5A power to the Raspberry Pi 5, resolving issues like lag and rebooting caused by insufficient power supply.
💡People Also Read: LanderPi: AI Models + SLAM Navigation + 3D Vision in One Robot
As robotics becomes more complex, single-controller systems are struggling to keep up with the demands for both high performance and real-time responsiveness. A dual-brain architecture solves this limitation by splitting tasks intelligently, delivering more efficient workload distribution and a more flexible, reliable development platform.
Hiwonder’s MentorPi, built on the "Raspberry Pi 5 + STM32 controller" dual-brain setup, perfectly embodies this concept. If you're seeking a control solution that seamlessly balances intelligence with precise execution, MentorPi is the answer.