Ballistic Multibody Estimator for 2D Open Kinematic Chain
This provides a lightweight estimator for free-flying robots, useful in aerospace and entertainment, but it is incremental as it builds on existing Kalman filter methods.
The paper tackled state estimation for free-flying open kinematic chains by proposing a cascade of two Kalman filters using ballistic motion, IMU, and encoder data, which outperformed EKF and UKF in tracking performance and computational time in simulations.
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.