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This research presents a hierarchical control framework specifically designed to stabilize two-wheeled inverted pendulum robots during asymmetric object manipulation, addressing critical challenges like odometry drift and dynamic instability. By fusing offboard vision data with low-latency onboard inertial feedback, the system enables precise navigation and payload handling on uneven terrain without relying solely on unreliable wheel-based sensors. The work advances robotic autonomy by solving coupled dynamics between balancing control and arm actuation, offering a robust solution for mobile manipulators in unstructured environments.
LQR-ArUco Fusion: Robust Hierarchical Control for Navigation and Asymmetric Manipulation in Two-Wheeled Robots
arXiv:2609.35700v1 Announce Type: new 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.
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.
6 pages, 9 figures, 2 tables. Accepted for presentation at the 2026 IEEE International Conference on Intelligent Navigation and Systems (CINS), Dubai, UAE
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2609.35700 [cs.RO]
(or arXiv:2609.35700v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.35700 arXiv-issued DOI via DataCite (pending registration)
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