Target Spacecraft

Target spacecraft research focuses on enabling safe and efficient interactions with spacecraft, crucial for on-orbit servicing, debris removal, and rendezvous missions. Current efforts concentrate on developing advanced control algorithms (e.g., model predictive control, weighted controllers) for precise maneuvering and docking, alongside robust pose estimation techniques leveraging neural networks and neural radiance fields to handle unknown or non-cooperative targets. These advancements are vital for improving the autonomy and reliability of space operations, reducing mission risks, and extending the lifespan of valuable assets.

Papers

June 7, 2024

Optimal path planning and weighted control of a four-arm robot in on-orbit servicing
Celia Redondo-Verdú, José L. Ramón, Álvaro Belmonte-Baeza, Jorge Pomares, Leonard Felicetti
External Control Trajectory Optimization Optimal Path Planning Orbit Servicing Target Spacecraft

May 21, 2024

Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations
Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer
Neural Radiance Field Pose Estimation Pose Estimation Network Proximity Operation Space Object Space Debris Removal Target Spacecraft

April 5, 2024

Convex MPC and Thrust Allocation with Deadband for Spacecraft Rendezvous
Pedro Taborda, Hugo Matias, Daniel Silvestre, Pedro Lourenço
Predictive Control Rendezvous Problem Band Selection Thrust Estimation RCS Thruster Target Spacecraft

February 26, 2023

Learning cross space mapping via DNN using large scale click-through logs
Wei Yu, Kuiyuan Yang, Yalong Bai, Hongxun Yao, Yong Rui
LeArning Abstract Deep Neural Network Click Through Rate Image Similarity Unified Deep Neural Network Target Spacecraft

October 20, 2022

ESPNN: A novel electronic stopping power neural-network code built on the IAEA stopping power database. I. Atomic targets
F. Bivort Haiek, A. M. P. Mendez, C. C. Montanari, D. M. Mitnik
Neural Network Target Spacecraft ESPnet ST Power Data

August 11, 2022

Cross Section Doppler Broadening prediction using Physically Informed Deep Neural Networks
Arthur Pignet, Luiz Leal, Vaibhav Jaiswal
Deep Learning Target Spacecraft

June 8, 2022

Adaptive Neural Network-based Unscented Kalman Filter for Robust Pose Tracking of Noncooperative Spacecraft
Tae Ha Park, Simone D'Amico
Unscented Kalman Filter Rendezvous Problem Pose Tracking Adaptive Neural Target Spacecraft

February 18, 2022

Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions
Majdi I. Radaideh, Hoang Tran, Lianshan Lin, Hao Jiang, Drew Winder, Sarma Gorti, Guannan Zhang, Justin Mach, Sarah Cousineau
Neural Network Model Calibration Constitutive Model Target Spacecraft Sparse Polynomial Cavitation Intensity Recognition

Target Spacecraft

Papers

Optimal path planning and weighted control of a four-arm robot in on-orbit servicing

Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations

Convex MPC and Thrust Allocation with Deadband for Spacecraft Rendezvous

Learning cross space mapping via DNN using large scale click-through logs

ESPNN: A novel electronic stopping power neural-network code built on the IAEA stopping power database. I. Atomic targets

Cross Section Doppler Broadening prediction using Physically Informed Deep Neural Networks

Adaptive Neural Network-based Unscented Kalman Filter for Robust Pose Tracking of Noncooperative Spacecraft

Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions