TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers

• Published in: 2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 1 - 7
• Main Authors: Nguyen, Khai, Schoedel, Sam, Alavilli, Anoushka, Plancher, Brian, Manchester, Zachary
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.05.2024
• Subjects: Collision avoidance; Convex functions; Hardware; Microcontrollers; Predictive control; Robots; Trajectory tracking
• DOI: 10.1109/ICRA57147.2024.10610987

Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC solver with a low memory footprint targeting the microcontrollers common on small robots. Our approach is based on the alternating direction method of multipliers (ADMM) and leverages the structure of the MPC problem for efficiency. We demonstrate TinyMPC's effectiveness by bench-marking against the state-of-the-art solver OSQP, achieving nearly an order of magnitude speed increase, as well as through hardware experiments on a 27 gram quadrotor, demonstrating high-speed trajectory tracking and dynamic obstacle avoidance. TinyMPC is publicly available at https://tinympc.org.