Selected work

Representative Papers

Optimal Design Framework for Distributed Array Using Magnetically-Actuated Satellite Swarm

May 22, 2026

This study addresses the challenge of multi-constraint coupling in the design of distributed aperture antennas for electromagnetic formation flying. The authors propose a system-level design framework that unifies phased array performance requirements with constraints on satellite mass, power consumption, coil geometry, and formation-keeping dynamics into a single modeling framework. Notably, for the first time, formation-keeping metrics derived from distributed control simulations are incorporated into the aperture maximization problem, yielding a joint optimization model that accounts for aperture size, power allocation, coil parameters, and sidelobe envelope specifications. Leveraging a static mesh reference structure, the framework efficiently computes feasible apertures under fixed system mass. Case studies demonstrate that at a 0.15 m inter-satellite spacing, power generation and coil geometry dominate the design constraints, whereas at 0.60 m, coil loading tends to exceed limits—validating the framework’s capability to effectively evaluate and optimize aperture configurations under complex, coupled constraints.

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CLARE: Classification-based Regression for Electron Temperature Prediction

Mar 12, 2026

This study addresses the longstanding challenge of accurately predicting electron temperature (Te)—a critical parameter in upper atmospheric space weather—by introducing an innovative classification-based regression approach. Leveraging AKEBONO satellite observations and solar-geomagnetic indices, the method discretizes the continuous Te prediction task into 150 distinct intervals, thereby enabling both high predictive accuracy and robust uncertainty quantification. This strategy overcomes key limitations of conventional regression models. On the test set, the proposed model achieves a prediction accuracy of 69.67% within ±10% of the true Te values, with a storm-time accuracy of 46.17% during geomagnetic disturbances—representing a 6.46% improvement over traditional regression approaches.

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Free-Flying Crew Cooperative Robots on the ISS: A Joint Review of Astrobee, CIMON, and Int-Ball Operations

Feb 11, 2026

This study addresses the need to enhance on-orbit collaborative capabilities of free-flying robots aboard the International Space Station by systematically analyzing the design objectives, operational experience, and full-lifecycle management of three intravehicular robotic platforms: NASA’s Astrobee, DLR’s CIMON, and JAXA’s Int-Ball. Conducted collaboratively by the three respective teams—the first such joint effort—it identifies common challenges and convergent strategies across system architecture, human–robot interfaces, autonomous navigation, and mission management. The findings synthesize a comprehensive knowledge framework spanning design through operations and establish cooperative design principles and technical references for future spaceborne intelligent robots, offering practical guidance for the development of next-generation space station robotic systems.

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Towards the Automation in the Space Station: Feasibility Study and Ground Tests of a Multi-Limbed Intra-Vehicular Robot

Dec 28, 2025

Astronauts aboard the International Space Station (ISS) expend substantial time on repetitive logistics tasks—such as cargo preparation and transport—impeding critical scientific experimentation. To address this, we propose the Multi-Limb Intra-Vehicular Robot (MLIVR), the first system to achieve end-to-end autonomous coordination between a mobile platform and a multi-degree-of-freedom manipulator in microgravity cabin environments. MLIVR integrates 3D motion planning, microgravity desktop simulation, autonomous navigation, and task scheduling. Evaluated via high-fidelity simulation and physical prototype testing under near-microgravity conditions, it demonstrates robust material transport and organization capabilities, achieving a task execution accuracy of 92.3% and reducing human-in-the-loop interventions by 76%. This work establishes an engineering-feasible pathway toward automated in-orbit logistics for the ISS, significantly enhancing intra-vehicular operational efficiency and optimizing astronaut time utilization.

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Moving object detection from multi-depth images with an attention-enhanced CNN

Dec 04, 2025

Manual verification in wide-field solar system surveys severely limits the efficiency of moving object detection. Method: This paper proposes an end-to-end multi-input convolutional neural network (CNN) architecture incorporating the Convolutional Block Attention Module (CBAM), enabling direct processing of multi-depth, multi-frame stacked images and adaptive enhancement of salient motion features in both spatial and channel dimensions. The method eliminates traditional manual screening, achieving fully automated detection and classification from raw image sequences. Results: Evaluated on approximately 2,000 real survey images, the model achieves 98.9% accuracy and an AUC of 0.992. With optimized detection thresholds, it maintains high recall while reducing human verification effort by over 99%. This work significantly advances the automation level and operational efficiency of moving object discovery in astronomical surveys.

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Recent publications

Latest Papers

Optimal Design Framework for Distributed Array Using Magnetically-Actuated Satellite Swarm

May 22, 2026

This study addresses the challenge of multi-constraint coupling in the design of distributed aperture antennas for electromagnetic formation flying. The authors propose a system-level design framework that unifies phased array performance requirements with constraints on satellite mass, power consumption, coil geometry, and formation-keeping dynamics into a single modeling framework. Notably, for the first time, formation-keeping metrics derived from distributed control simulations are incorporated into the aperture maximization problem, yielding a joint optimization model that accounts for aperture size, power allocation, coil parameters, and sidelobe envelope specifications. Leveraging a static mesh reference structure, the framework efficiently computes feasible apertures under fixed system mass. Case studies demonstrate that at a 0.15 m inter-satellite spacing, power generation and coil geometry dominate the design constraints, whereas at 0.60 m, coil loading tends to exceed limits—validating the framework’s capability to effectively evaluate and optimize aperture configurations under complex, coupled constraints.

0 citationsRead paper

CLARE: Classification-based Regression for Electron Temperature Prediction

Mar 12, 2026

This study addresses the longstanding challenge of accurately predicting electron temperature (Te)—a critical parameter in upper atmospheric space weather—by introducing an innovative classification-based regression approach. Leveraging AKEBONO satellite observations and solar-geomagnetic indices, the method discretizes the continuous Te prediction task into 150 distinct intervals, thereby enabling both high predictive accuracy and robust uncertainty quantification. This strategy overcomes key limitations of conventional regression models. On the test set, the proposed model achieves a prediction accuracy of 69.67% within ±10% of the true Te values, with a storm-time accuracy of 46.17% during geomagnetic disturbances—representing a 6.46% improvement over traditional regression approaches.

0 citationsRead paper

Free-Flying Crew Cooperative Robots on the ISS: A Joint Review of Astrobee, CIMON, and Int-Ball Operations

Feb 11, 2026

This study addresses the need to enhance on-orbit collaborative capabilities of free-flying robots aboard the International Space Station by systematically analyzing the design objectives, operational experience, and full-lifecycle management of three intravehicular robotic platforms: NASA’s Astrobee, DLR’s CIMON, and JAXA’s Int-Ball. Conducted collaboratively by the three respective teams—the first such joint effort—it identifies common challenges and convergent strategies across system architecture, human–robot interfaces, autonomous navigation, and mission management. The findings synthesize a comprehensive knowledge framework spanning design through operations and establish cooperative design principles and technical references for future spaceborne intelligent robots, offering practical guidance for the development of next-generation space station robotic systems.

0 citationsRead paper

Towards the Automation in the Space Station: Feasibility Study and Ground Tests of a Multi-Limbed Intra-Vehicular Robot

Dec 28, 2025

Astronauts aboard the International Space Station (ISS) expend substantial time on repetitive logistics tasks—such as cargo preparation and transport—impeding critical scientific experimentation. To address this, we propose the Multi-Limb Intra-Vehicular Robot (MLIVR), the first system to achieve end-to-end autonomous coordination between a mobile platform and a multi-degree-of-freedom manipulator in microgravity cabin environments. MLIVR integrates 3D motion planning, microgravity desktop simulation, autonomous navigation, and task scheduling. Evaluated via high-fidelity simulation and physical prototype testing under near-microgravity conditions, it demonstrates robust material transport and organization capabilities, achieving a task execution accuracy of 92.3% and reducing human-in-the-loop interventions by 76%. This work establishes an engineering-feasible pathway toward automated in-orbit logistics for the ISS, significantly enhancing intra-vehicular operational efficiency and optimizing astronaut time utilization.

0 citationsRead paper

Moving object detection from multi-depth images with an attention-enhanced CNN

Dec 04, 2025

Manual verification in wide-field solar system surveys severely limits the efficiency of moving object detection. Method: This paper proposes an end-to-end multi-input convolutional neural network (CNN) architecture incorporating the Convolutional Block Attention Module (CBAM), enabling direct processing of multi-depth, multi-frame stacked images and adaptive enhancement of salient motion features in both spatial and channel dimensions. The method eliminates traditional manual screening, achieving fully automated detection and classification from raw image sequences. Results: Evaluated on approximately 2,000 real survey images, the model achieves 98.9% accuracy and an AUC of 0.992. With optimized detection thresholds, it maintains high recall while reducing human verification effort by over 99%. This work significantly advances the automation level and operational efficiency of moving object discovery in astronomical surveys.

0 citationsRead paper