Byungjin Lee

This paper proposes a strategic approach to simultaneously achieve collision avoidance and non- line-ofsight (NLOS) clearance during the reconfiguration of multivehicle flight trajectories. In many trajectory reconfiguration cases, hazardous situations are defined as constraints, which are typically convexified through a convex relaxation process. For securing NLOS clearance, the paper exploits a sophisticated technique to convexify the constraint related to the line-of-sight disruption phenomenon by developing the conic relation within a relative state framework. These constraints are then applied to the model predictive control (MPC)-based guidance method to generate trajectories for each vehicle, incorporating an indirect linear relation designed to connect vehicle dynamics and the MPC-guided trajectories. The proposed convexification scheme is shown to offer advantages in implementing a one-shot algorithm compared to other iterative methods. The effectiveness of the proposed method is verified through simulations, demonstrating superior performance in collision and line-ofsight disruptive scenarios.

This study introduces a pose estimation algorithm integrating an Inertial Navigation System (INS) with an Alternating Current (AC) magnetic field-based navigation system, referred to as the Magnetic Positioning System (MPS), evaluated using a 6 Degrees of Freedom (DoF) drone. The study addresses significant challenges such as the magnetic vector distortions and model uncertainties caused by motor noise, which degrade attitude estimation and limit the effectiveness of traditional Extended Kalman Filter (EKF)-based fusion methods. To mitigate these issues, a Tightly Coupled Unscented Kalman Filter (TC UKF) was developed to enhance robustness and navigation accuracy in dynamic environments. The proposed Unscented Kalman Filter (UKF) demonstrated a superior attitude estimation performance within a 6 m coil spacing area, outperforming both the MPS 3D LS (Least Squares) and EKF-based approaches. Furthermore, hyperparameters such as alpha, beta, and kappa were optimized using the Sequential Importance Resampling (SIR) process of the Particle Filter. This adaptive hyperparameter adjustment achieved improved navigation results compared to the default UKF settings, particularly in environments with high model uncertainty.

This study proposes a sensor integration design of magnetic positioning system (MPS) for achieving robust navigation performance. Despite consistent permeability of AC magnetic fields, signal strength that is inversely proportional to the square of the distance greatly constrains the operational range of the MPS. Additionally, the signal becomes vulnerable to environmental noise with deteriorated signal-to-noise ratio, which may bring about partial signal outage from some magnetic transmitter. In this background, this paper proposes the tightly coupled MPS/INS integrated navigation system to overcome these shortcomings. Instead of implementing isolated pose estimation via multiple magnetic measurements, it is designed each magnetic field vector is independently incorporated in the INS mechanization framework. As a result, successful filter correction has been guaranteed in the constrained magnetic measurement environments, thereby obtaining results that ensure pose estimation performance in an extended range with poor signal quality or even in a partial transmitter outage case. Besides, the proposed method contributes to demonstrate improved estimation performance in the navigation filter by providing more accurate magnetic predictions and measurement residuals compared to the traditional dipole technique. The proposed algorithm is demonstrated through trajectory experiments with an unmanned vehicle platform in outdoor domain with different ranges and partial signal occlusion scenario. When compared with the reference system, the robust navigation results using the MPS/INS TC algorithm exhibited pose estimation error of decimeters in position and 2 degree in attitude along with computational efficiency. Moreover, robust estimation is observed even in a partial signal occlusion in contrast with previous diverging results from MPS-only or MPS/INS loosely coupled system.

This study proposes an enhanced integration algorithm that combines the magnetic field-based positioning system (MPS—Magnetic Pose Estimation System) with an inertial system with the advantage of an invariant filter structure. Specifically, to mitigate the nonlinearity of the propagation model and perturbing effect from the estimated uncertainty, the formulation of the invariant Kalman filter was derived in detail. Then, experiments were conducted to validate the algorithm with an unmanned vehicle equipped with an IMU and MPS receiver. As a result, the navigation performance of the IEKF-based inertial and magnetic field integration system was presented and compared with the conventional Kalman filter results. Furthermore, the convergence and navigation performance were evaluated in the presence of state variable initialization errors. The findings indicate that the inertial and magnetic field coupled with the IEKF outperforms the typical KF approach, particularly when dealing with initial estimate uncertainties.

This paper explores the research topic of enhancing the reliability of unmanned mobile exploration using LiDAR SLAM. Specifically, it proposes a technique to analyze waypoints where 3D LiDAR SLAM can be smoothly performed in potential exploration areas and points where there is a risk of divergence in navigation estimation. The goal is to improve exploration performance by presenting a method that secures these candidate regions. The analysis employs a 3D geometric observability matrix and its condition number to discriminate waypoints. Subsequently, the discriminated values are applied to path planning, resulting in the derivation of a final destination path connecting waypoints with a satisfactory SLAM position and attitude estimation performance. To validate the proposed technique, performance analysis was initially conducted using the Gazebo simulator. Additionally, experiments were performed with an autonomous unmanned vehicle in a real-world environment.

This article presents a method for estimating the angular rate and attitude of high-spin objects with free maneuvering such as flying disks or spinning projectiles. A new framework is proposed that utilizes the estimated spin rate for simultaneous gyroscope calibration and 3-D attitude estimation. Previous studies on spin rate estimation based on periodicity analysis, comparatively, have mostly overlooked the phase anomaly effect that may occur when the attitude changes in the nonorthogonal axes. Therefore, this study explains the newly discovered phase anomaly and makes compensation to improve the spin rate estimation performance when the spinning object rotates without any axial constraint. Additionally, a new observation model is proposed, relating the roll rate and gyroscope scale factor using the estimated spin rate. Based on this model, an extended Kalman filter (EKF) is developed to calibrate the gyroscope scale factor and estimate 3-D attitude. To address the time-delay characteristics of the spinning rate used as a measurement, a two-stage cascade EKF (CEKF) is implemented. The performance of the proposed methods is validated through simulations of spinning projectiles at rates of 30 and 60 rotations per second, as well as empirical rotation experiments using turntable. This study distinguishes itself from previous research by including explicit orthogonal rotation with respect to the principal rotation axis, and its performance is demonstrated accordingly.

This paper proposes a new navigation algorithm that integrates a magnetic pose estimation system (MPS), an IMU, and a range sensor to provide stable navigation performance in unstructured indoor environment. Moreover, we implement a real-time navigation system to apply these navigation algorithms to aerial vehicle. In this paper, the magnetic field vector is modeled using an algorithm called MPS, and the position and attitude are estimated through the least squares method. However, while analyzing the results of this system, it was confirmed that navigation performance deteriorated due to magnetic field distortion in an unstructured indoor environment. To improve these limitations, we present a new type of EKF (Extended Kalman Filter) algorithm that integrates an MPS, an IMU and a range sensor. Finally, in order to verify the algorithm proposed in this paper, a real-time navigation system is designed, and ground and flight experiments are conducted.