The StackForce Large Bipedal Wheeled Robot is a programmable legged robot kit developed on the StackForce lightweight robotics platform. Powered by StackForce controller boards, high-power brushless motor drivers, servo modules, and CAN communication, it delivers robust stability and smooth locomotion across indoor and outdoor terrains.

Equipped with joint motors, hub motors, and advanced control algorithms, the robot maintains balance in dynamic conditions such as slopes, sharp turns, and rough surfaces. The latest version integrates LQR (Linear Quadratic Regulator) control, providing faster response, stronger robustness, and eliminating the tedious parameter tuning required by traditional PID control.

Features

• Powerful Stability – Real-time kinematics and adaptive algorithms ensure reliable self-balancing.
• Advanced Control – Supports LQR, PID, and VMC algorithms for dynamic balance and smooth locomotion.
• Integrated Joint Motors – High-torque StackForce servo + drive integration with 11Nm torque and 420rpm speed.
• High-Power Hub Motor Drive – 25A brushless drivers with Hall sensors, delivering strong torque and low-latency response.
• CAN + 485 Communication – Fast, stable, and reliable data exchange between motors and controllers.
• Lightweight, Modular Design – Based on the StackForce platform for seamless integration with sensors and modules.
• Open-Source Ready – Full code access, documentation, and tutorials for STEM robotics education and R&D.

Course Description

The StackForce Bipedal Wheeled Robot Course offers a complete hands-on learning path from mechanical assembly to advanced control theory. Students and developers learn how to assemble, configure, and program the robot while mastering modern control methods.

Highlights:

• Fundamentals – Mechanical setup, balance principles, and control system architecture.
• Basic Motion Control – Forward, backward, turning, and slope adaptation.
• Self-Stabilization – Implementing PID, LQR, and VMC algorithms for balance.
• Advanced Robotics Control – Gait planning, trajectory design, nonlinear control, and hybrid wheel-leg dynamics.
• Special Actions – Jumping, autonomous recovery, and force-controlled leg motion.
• Comparative Studies – Experimentally comparing PID vs LQR vs VMC to understand control performance.
• Capstone Project – Optimizing stability and agility in complex terrains using advanced algorithms.

Learners gain not only coding skills but also robot dynamics, trajectory planning, and feedback control knowledge, bridging theory with real robotics practice.

Application

• STEM Education – Teaching robotics, kinematics, and modern control theory (PID/LQR/VMC).
• Research & Development – Advanced testbed for control algorithms, balance, and dynamics studies.
• DIY & Makers – Open-source legged robot kit for hobbyists and robotics competitions.
• Robotics Projects – Real-world testing of locomotion strategies on flat, sloped, and rugged terrains.