Ballistic Multibody Estimator for 2D Open Kinematic Chain

TL;DR

This paper introduces a lightweight, cascade Kalman filter-based estimator for free-flying robots with open kinematic chains, leveraging ballistic motion and sensor feedback for improved state estimation.

Contribution

It proposes a novel cascade Kalman filter structure that avoids nonlinear models, enhancing accuracy and efficiency in estimating free-flying multibody system states.

Findings

Outperforms EKF and UKF in tracking accuracy

Reduces computational time compared to traditional filters

Effective under varied physical parameters

Abstract

Applications of free-flying robots range from entertainment purposes to aerospace applications. The control algorithm for such systems requires accurate estimation of their states based on sensor feedback. The objective of this paper is to design and verify a lightweight state estimation algorithm for a free-flying open kinematic chain that estimates the state of its center-of-mass and its posture. Instead of utilizing a nonlinear dynamics model, this research proposes a cascade structure of two Kalman filters (KF), which relies on the information from the ballistic motion of free-falling multibody systems together with feedback from an inertial measurement unit (IMU) and encoders. Multiple algorithms are verified in the simulation that mimics real-world circumstances with Simulink. Several uncertain physical parameters are varied, and the result shows that the proposed estimator outperforms EKF and UKF in terms of tracking performance and computational time.