Two wheeled robot uses vision and sensors to balance and carry payload

LQR-ArUco Fusion: Robust Hierarchical Control for Navigation and Asymmetric Manipulation in Two-Wheeled Robots

Robotics

Summary

Balancing two-wheeled robots is hard, especially when they hold objects on one side. The authors designed a control system that uses a camera tracking special markers to help the robot know where it is, avoiding errors from its own movement sensors. At the same time, the robot uses fast feedback from its internal sensors to stay balanced and handle bumps or uneven ground. This system lets the robot move precisely, stay upright, and carry things safely, even on tricky surfaces.

What this means in practice

Authors

Anupam Chatterjee, Arpita Kumari

Abstract

We propose a hierarchical control framework to address severe dynamic instabilities and navigational drift that arise when a two-wheeled inverted pendulum (TWIP) robot attempts asymmetric object manipulation. While two-wheeled platforms are highly manoeuvrable, their constant balancing adjustments make onboard odometry highly unreliable for precise navigation. Furthermore, the addition of a side-mounted robotic arm introduces unactuated lateral roll moments when a payload is lifted, a challenge heavily compounded on uneven terrain. To solve these coupled problems, our architecture divides the workload. An offboard vision system tracks overhead ArUco markers to provide high-latency global waypoint navigation, bypassing odometry drift. Simultaneously, a low-latency onboard control loop rejects active physical disturbances using inertial and encoder data. In our physical experiments, this dual-loop approach enabled the custom-built robot to navigate accurately, reject transient impacts from speed bumps, adapt to a dynamic seesaw ramp, and carry a payload securely without falling over its narrow wheelbase.