56 citations · 312 across the 19 of their papers we have counts for
8 papers · 1 filter
Multidisciplinary Design Optimization Approach to Integrated Space Mission Planning and Spacecraft Design
Masafumi Isaji, Yuji Takubo, Koki Ho
Space mission planning and spacecraft design are tightly coupled and need to be considered together for optimal performance; however, this integrated optimization problem results i…
Flexibility Management for Space Logistics via Decision Rules
Hao Chen, Brian Gardner, Paul Grogan +1
This paper develops a flexibility management framework for space logistics mission planning under uncertainty through decision rules and multi-stage stochastic programming. It aims…
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design
Yuji Takubo, Hao Chen, Koki Ho
This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure depl…
Space Exploration Architecture and Design Framework for Commercialization
Hao Chen, Melkior Ornik, Koki Ho
The trend of space commercialization is changing the decision-making process for future space exploration architectures, and there is a growing need for a new decision-making frame…
On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff
Tristan Sarton du Jonchay, Hao Chen, Masafumi Isaji +2
This paper proposes an on-orbit servicing logistics optimization framework that is capable of performing the short-term operational scheduling and long-term strategic planning of s…
Framework for Modeling and Optimization of On-Orbit Servicing Operations under Demand Uncertainties
Tristan Sarton du Jonchay, Hao Chen, Onalli Gunasekara +1
This paper develops a framework that models and optimizes the operations of complex on-orbit servicing infrastructures involving one or more servicers and orbital depots to provide…
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers
Keidai Iiyama, Kento Tomita, Bhavi A. Jagatia +2
This research proposes a new integrated framework for identifying safe landing locations and planning in-flight divert maneuvers. The state-of-the-art algorithms for landing zone s…
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
Kento Tomita, Katherine A. Skinner, Koki Ho
Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the…