arXiv subjects

Yasuhiro Yoshimura

Publications and source records attributed to Yasuhiro Yoshimura.

12 recordsLinked to original sources

arXiv2026

Assessing Collision Probability in Low-Thrust Deorbit

End-of-life support of satellites is necessary to improve post-mission-disposal compliance rates for maintaining space environment. Deorbit mission with low thrust, e.g. a laser, induces a low-level deceleration on the target object that gradually lowers the target altitude. Since such a low-thrust trajectory is time-consuming, the risk of collision greatly influences the mission success rate. In this context, this paper assesses the collision risk during deorbit trajectories with low thrust. Furthermore, parametric studies for the relationship between the re-entry time and the risk of collision are performed.

eess.SY↗

arXiv2026

Improving Observability of Relative Orbit Estimation Using Bearing Measurements and Light Curves

Relative orbit estimation using optical observations is a key technology for on-orbit servicing missions. In the far-range phase, the target appears as an unresolved point source, providing only bearing angles (azimuth and elevation) from the servicing satellite. Angles-only navigation is inherently challenging due to the weak observability of the relative range. To address this limitation, this study investigates the effectiveness of an estimation scheme that fuses photometric light curve data with bearing measurements. Since the light intensity depends on the relative distance, fusing light curves enhances the observability of the relative state. The Ashikhmin-Shirley model is used as the optical reflectance model, and observability analysis is conducted with the Fisher information matrix. Numerical simulations involving different target geometries, a flat plate and a box-wing satellite, demonstrate that integrating light curve measurements significantly enhances observability and enables faster convergence compared to conventional state estimation methods.

eess.SY↗

arXiv2026

Attitude Estimation from Photometric Data using Gaussian Process Regression

The rapid growth of resident space objects in Earth's orbit has intensified the need for advanced space situational awareness and space domain awareness to manage satellite traffic and prevent collisions. Attitude estimation is critical for accurate state propagation, as non-gravitational forces like solar radiation pressure and atmospheric drag depend on the object's attitude. This study explores using light curves, time variation of an object's brightness, to estimate a space object's attitude. Light curve inversion, traditionally used in astronomy, faces challenges when applied to resident space objects due to their non-convex shapes and specular reflections. Conventional methods for attitude estimation often assume known shape and surface parameters, which are usually unknown for space debris generated by a collision or breakup. To address this issue, this study proposes the estimation method combining Gaussian process regression with the unscented Kalman filter. This study uses Gaussian process regression for a non-parametric observation model, enhancing robustness against unknown surface parameters. Numerical examples consider a box-wing object in a geosynchronous orbit and demonstrate that the proposed method has better estimation accuracy than a conventional unscented Kalman filter. The numerical simulation results also represent the attitude estimation robust against uncertainties in surface properties, contributing to practical scenarios in space situational awareness and space domain awareness where the object parameters are unknown.

eess.SY↗

arXiv2026

Adaptive Relative Orbit Control Considering Laser Ablation Uncertainty

This study proposes a relative orbit control law for laser debris removal missions considering the uncertainties of laser ablation and atmospheric drag. A removal spacecraft irradiates laser pulses to a target debris to generate the ablation force for deorbiting. The deorbiting force lowers the target altitude, and the removal spacecraft must follow it to maintain its relative position for continuous laser irradiation. The difficulty stems from uncertainties of the magnitude of laser ablation and external disturbances such as atmospheric drag. To tackle this problem, this study derives an adaptive control method using the Gaussian process regression to cancel the uncertainties with a nonparametric regression model. Numerical simulations verify the proposed control law under the uncertainties of laser ablation and atmospheric drag. The proposed control law can contribute to the realization of a safer and more secure mission not only for laser debris removal missions, but also for other on-orbit services.

eess.SY↗

arXiv2026

Adaptive Attitude Estimation for Multiple-Surface Object Using Light Curve Glints

Light curve inversion enables the estimation of orbit, attitude, optical properties, and shape of space objects. Because a light curve is the temporal evolution of a scalar apparent magnitude, the estimation problem can be ill-posed owing to the non-uniqueness of the attitude that reproduces a given light curve. The initial estimate in Kalman filtering can therefore be sensitive, and, depending on the object properties, observation geometry, and number of estimated parameters, an inaccurate initial estimate may lead to divergence of the filter. A previous study uses a sudden change of light curves, called glint, to constrain the range of attitude estimate. The current paper extends the attitude estimation method using glint for multiple-surface objects. Such objects have multiple attitudes to yield glint, and the attitude estimate is not uniquely determined. To address this issue, this paper employs the interacting multiple model (IMM) algorithm that runs multiple parallel filters with model interaction in the estimation sequence. Each filter assumes that the glint occurs on the corresponding surface. The mode probability is updated by the likelihood of each filter, determining the correctness of the hypotheses. Furthermore, the mixing step in the IMM allows interaction among the filters through a transition probability matrix, enabling adaptation to the time-varying glint source. Numerical simulations are conducted for a box satellite in a geosynchronous orbit. Monte Carlo trials with initial attitude errors of up to 80~deg show that the proposed method improves the convergence rate from 10\% for a single surface filter to 73%, and the mixing step is shown to be essential, since the convergence rate drops to 40% when it is removed.

eess.SY↗

arXiv2026

Data-Driven Prediction of Chaotic Transition in Periapsis Poincar\'e Maps

Chaotic trajectories in multi-body dynamical systems play a crucial role in designing low-energy trajectories in astrodynamics. However, predicting these trajectories is inherently difficult, as small errors in initial conditions can grow exponentially, making long-term predictions unreliable. This study introduces a novel methodology using Dynamic Mode Decomposition (DMD) to predict chaotic transitions in the periapsis Poincar\'e map of the circular restricted three-body problem (CRTBP). Unlike standard DMD approaches that model continuous equations of motion, the proposed method approximates deformations in a low-dimensional Poincar\'e map, enabling trajectory prediction and revealing transition structures. Two approaches are developed: the Local Deformation Map-based DMD (LDMD) and the Global Deformation Map-based DMD (GDMD). LDMD constructs discrete maps to track local deformations of periapsis sets, while GDMD captures global deformations using widely distributed data. A key advantage of this framework is that it approximates nonlinear chaotic transport using a linear operator, which enables fast prediction of periapsis evolution via matrix powers and direct access to geometric structures. To validate the proposed method, the deformation map is applied to design ballistic transfer trajectories to the Moon using a targeting strategy, demonstrating its practical relevance in astrodynamics. This work highlights the potential of data-driven modeling to bridge chaotic dynamics with systematic trajectory design.

nlin.CD↗

arXiv2025

VLG-Loc: Vision-Language Global Localization from Labeled Footprint Maps

This paper presents Vision-Language Global Localization (VLG-Loc), a novel global localization method that uses human-readable labeled footprint maps containing only names and areas of distinctive visual landmarks in an environment. While humans naturally localize themselves using such maps, translating this capability to robotic systems remains highly challenging due to the difficulty of establishing correspondences between observed landmarks and those in the map without geometric and appearance details. To address this challenge, VLG-Loc leverages a vision-language model (VLM) to search the robot's multi-directional image observations for the landmarks noted in the map. The method then identifies robot poses within a Monte Carlo localization framework, where the found landmarks are used to evaluate the likelihood of each pose hypothesis. Experimental validation in simulated and real-world retail environments demonstrates superior robustness compared to existing scan-based methods, particularly under environmental changes. Further improvements are achieved through the probabilistic fusion of visual and scan-based localization.

cs.RO↗

arXiv2025

GSplatVNM: Point-of-View Synthesis for Visual Navigation Models Using Gaussian Splatting

This paper presents a novel approach to image-goal navigation by integrating 3D Gaussian Splatting (3DGS) with Visual Navigation Models (VNMs), a method we refer to as GSplatVNM. VNMs offer a promising paradigm for image-goal navigation by guiding a robot through a sequence of point-of-view images without requiring metrical localization or environment-specific training. However, constructing a dense and traversable sequence of target viewpoints from start to goal remains a central challenge, particularly when the available image database is sparse. To address these challenges, we propose a 3DGS-based viewpoint synthesis framework for VNMs that synthesizes intermediate viewpoints to seamlessly bridge gaps in sparse data while significantly reducing storage overhead. Experimental results in a photorealistic simulator demonstrate that our approach not only enhances navigation efficiency but also exhibits robustness under varying levels of image database sparsity.

cs.RO↗

arXiv2024

Opt-in Camera: Person Identification in Video via UWB Localization and Its Application to Opt-in Systems

This paper presents opt-in camera, a concept of privacy-preserving camera systems capable of recording only specific individuals in a crowd who explicitly consent to be recorded. Our system utilizes a mobile wireless communication tag attached to personal belongings as proof of opt-in and as a means of localizing tag carriers in video footage. Specifically, the on-ground positions of the wireless tag are first tracked over time using the unscented Kalman filter (UKF). The tag trajectory is then matched against visual tracking results for pedestrians found in videos to identify the tag carrier. Technically, we devise a dedicated trajectory matching technique based on constrained linear optimization, as well as a novel calibration technique that handles wireless tag-camera calibration and hyperparameter tuning for the UKF, which mitigates the non-line-of-sight (NLoS) issue in wireless localization. We implemented the proposed opt-in camera system using ultra-wideband (UWB) devices and an off-the-shelf webcam. Experimental results demonstrate that our system can perform opt-in recording of individuals in real-time at 10 fps, with reliable identification accuracy in crowds of 8-23 people in a confined space.

cs.RO↗

arXiv2024

Pulse Width Modulation Method Applied to Nonlinear Model Predictive Control on an Under-actuated Small Satellite

Among various satellite actuators, magnetic torquers have been widely equipped for stabilization and attitude control of small satellites. Although magnetorquers are generally used with other actuators, such as momentum wheels, this paper explores a control method where only a magnetic actuation is available. We applied a nonlinear optimal control method, Nonlinear Model Predictive Control (NMPC), to small satellites, employing the generalized minimal residual (GMRES) method, which generates continuous control inputs. Onboard magnetic actuation systems often find it challenging to produce smooth magnetic moments as a control input; hence, we employ the Pulse Width Modulation (PWM) method, which discretizes a control input and reduces the burden on actuators. In our case, the PWM approach discretizes control torques generated by the NMPC scheme. This study's main contributions are investigating the NMPC and the GMRES method applied to small spacecraft and presenting the PWM control system's feasibility.

eess.SY↗

arXiv2024

Nonlinear Model Predictive Detumbling of Small Satellites with a Single-axis Magnetorquer

Various actuators are used in spacecraft to achieve attitude stabilization, including thrusters, momentum wheels, and control moment gyros. Small satellites, however, have stringent size, weight, and cost constraints, which makes many actuator choices prohibitive. Consequently, magnetic torquers have commonly been applied to spacecraft to attenuate angular rates. Approaches for dealing with under-actuation due to magnetic control torque's dependency on the magnetic field and required high magnetic flux densities have been previously considered. Generally speaking, control of a satellite that becomes under-actuated as a result of on-board failures has been a recurrent theme in the literature. Methods for controlling spacecraft with fewer actuators than degrees of freedom are increasingly in demand due to the increased number of small satellite launches. Magnetic torquers have been extensively investigated for momentum management of spacecraft with momentum wheels and for nutation damping of spin satellites, momentum-biased, and dual-spin satellites. Nonetheless, severely under-actuated small spacecraft that carry only a single-axis magnetic torquer have not been previously treated. This note considers the detumbling of a small spacecraft using only a single-axis magnetic torquer. Even with a three-axis magnetic torquer, the spacecraft is under-actuated, while, in the case of only a single axis magnetic torquer, the problem is considerably more demanding. Our note examines the feasibility of spacecraft attitude control with a single-axis magnetic torquer and possible control methods that can be used.

eess.SY↗

arXiv2024

Model Predictive Approach for Detumbling an Underactuated Satellite

This research proposes an innovative approach to detumble satellites' triple-axis angular velocities with only one single-axis magnetic torquer. Since magnetic torque is generated perpendicularly to magnetorquers, no intended control torque along the magnetorquer can be produced, which makes systems underactuated. Our paper introduces a control method using Model Predictive Control (MPC) and compares it with B-dot control algorithm. By applying these control laws to Kyushu University Light Curve Inversion (Q-Li) Demonstration Satellite in numerical simulations, we describe the applicability of these control laws to underactuated systems.

eess.SY↗