Flexible robot arms use fewer sensors for precise movement control
Flexibility-Aware Robotic Manipulation: Deformation Estimation, Kinematics, and Control via Distributed Inertial Measurement
Robotics
Summary
Robot arms that bend or flex are harder to control because traditional sensors only measure joint angles, missing how the arm itself bends. The authors developed a method that uses a few carefully placed motion sensors to track how the robot’s flexible parts move. This data helps the robot understand its own shape in real time and control its movements more accurately. Tests showed the system reduces errors and uses fewer sensors compared to older methods.
What this means in practice
- •For industrial robot builders: Design flexible robotic arms with minimal sensors that accurately estimate bending for precision tasks.
- •For automation system integrators: Implement real-time deformation sensing for hydraulic manipulators to improve control without excessive sensing hardware.
Authors
Amir Hossein Barjini, Sadeq Yaqubi, Pauli Mustalahti, Jouni Mattila
Abstract
Flexible robot links challenge the rigid-body assumption, making joint measurements alone insufficient for accurate manipulation. This paper introduces a flexibility-aware manipulation framework that connects structural dynamics directly to sensing, kinematics, and control. Instead of prescribing a dense inertial measurement unit (IMU) network, the dominant vibration modes of each flexible link are first identified from response-convergence and excitation-bandwidth criteria. Under a full-rank modal observability condition, $m$ retained modes require only $m$ independent slope measurements and one reference IMU, defining the minimum observable IMU configuration. The resulting measurements are used to reconstruct the continuous link deformation, which is then incorporated into flexibility-aware forward kinematics and a deformation-informed partial differential equation-based controller. Experiments on hydraulic single- and double-link manipulators evaluate the framework. On the long-reach double-link platform, the proposed kinematics achieves end-effector root mean square errors of $0.0107$~m, $0.0194$~m, and $0.0102$~rad while using fewer sensors than the piecewise-rigid method. Closed-loop reaching experiments further demonstrate real-time integration of IMU-estimated joint states and reconstructed deformation into the proposed controller. The results show that dominant modal information can determine sensing requirements and enable real-time flexibility-aware robotic manipulation.