Koki Ho

Seungyeop Han, Zachary Grieser, Shoji Yoshikawa et al.

This paper introduces a Markov chain-based approach for the analysis and optimization of spare-management policies in large-scale satellite constellations. Focusing on the direct strategy, we model spare replenishment as a periodic-review reorder-point/order-quantity policy, where spares are deployed directly to constellation planes. The stochastic behavior of satellite failures and launch vehicle lead times is captured through Markov representations of both failure and replenishment dynamics. Based on this efficient and accurate framework, we construct and solve an optimization problem aimed at minimizing operational costs. The effectiveness of the proposed method is demonstrated through a case study using a real-world mega-constellation.

Travis Driver, Kento Tomita, Koki Ho et al.

Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions. Current state-of-the-practice methods rely on high-fidelity, a priori terrain maps, which require extensive human-in-the-loop verification and expensive reconnaissance campaigns to resolve mapping uncertainties. We propose a novel safety mapping paradigm that leverages deep semantic segmentation techniques to predict landing safety directly from a single monocular image, thus reducing reliance on high-fidelity, a priori data products. We demonstrate precise and accurate safety mapping performance on real in-situ imagery of prospective sample sites from the OSIRIS-REx mission.

Pauline C. M. Jakob, Seiichi Shimizu, Shoji Yoshikawa et al.

The recent growing trend to develop large-scale satellite constellations (i.e., mega-constellation) with low-cost small satellites has brought the need for an efficient and scalable maintenance strategy decision plan. Traditional spare strategies for satellite constellations cannot handle these mega-constellations due to their limited scalability in number of satellites and/or frequency of failures. In this paper, we propose a novel spare strategy using an inventory management approach. We consider a set of parking orbits at a lower altitude than the constellation for spare storage, and model satellite constellation spare strategy problem using a multi-echelon (s,Q)-type inventory policy, viewing Earth's ground as a supplier, parking orbits as warehouses, and in-plane spare stocks as retailers. This inventory model is unique in that the parking orbits (warehouses) drift away from the orbital planes over time due to orbital mechanics' effects, and the in-plane spare stocks (retailers) would receive the resupply from the closest (i.e., minimum waiting time) available warehouse at the time of delivery. The parking orbits (warehouses) are also resupplied from the ground (supplier) with stochastic lead time caused by the order processing and launch opportunities, leveraging the cost saving effects by launching many satellites in one rocket (i.e., batch launch discount). The proposed analytical model is validated against simulations using Latin Hypercube Sampling. Furthermore, based on the proposed model, an optimization formulation is introduced to identify the optimal spare strategy, comprising the parking orbits characteristics and all locations policies, to minimize the maintenance cost of the system given performance requirements. The proposed model and optimization method are applied to a real-world case study of satellite mega-constellation to demonstrate their value.

Bindu Jagannatha, Koki Ho

Numerous high-thrust and low-thrust space propulsion technologies have been developed in the recent years with the goal of expanding space exploration capabilities; however, designing and optimizing a multi-mission campaign with both high-thrust and low-thrust propulsion options are challenging due to the coupling between logistics mission design and trajectory evaluation. Specifically, this computational burden arises because the deliverable mass fraction (i.e., final-to-initial mass ratio) and time of flight for low-thrust trajectories can can vary with the payload mass; thus, these trajectory metrics cannot be evaluated separately from the campaign-level mission design. To tackle this challenge, this paper develops a novel event-driven space logistics network optimization approach using mixed-integer linear programming for space campaign design. An example case of optimally designing a cislunar propellant supply chain to support multiple lunar surface access missions is used to demonstrate this new space logistics framework. The results are compared with an existing stochastic combinatorial formulation developed for incorporating low-thrust propulsion into space logistics design; our new approach provides superior results in terms of cost as well as utilization of the vehicle fleet. The event-driven space logistics network optimization method developed in this paper can trade off cost, time, and technology in an automated manner to optimally design space mission campaigns.

Kento Tomita, Koki Ho

Onboard terrain sensing and mapping for safe planetary landings often suffer from missed hazardous features, e.g., small rocks, due to the large observational range and the limited resolution of the obtained terrain data. To this end, this paper develops a novel real-time stochastic terrain mapping algorithm that accounts for topographic uncertainty between the sampled points, or the uncertainty due to the sparse 3D terrain measurements. We introduce a Gaussian digital elevation map that is efficiently constructed using the combination of Delauney triangulation and local Gaussian process regression. The geometric investigation of the lander-terrain interaction is exploited to efficiently evaluate the marginally conservative local slope and roughness while avoiding the costly computation of the local plane. The conservativeness is proved in the paper. The developed real-time uncertainty quantification pipeline enables stochastic landing safety evaluation under challenging operational conditions, such as a large observational range or limited sensor capability, which is a critical stepping stone for the development of predictive guidance algorithms for safe autonomous planetary landing. Detailed reviews on background and related works are also presented.

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 deployment planning, and space transportation scheduling. This problem involves a high-dimensional design space and is challenging especially with uncertainty present. To tackle this challenge, the developed framework has a hierarchical structure with reinforcement learning and network-based mixed-integer linear programming (MILP), where the former optimizes campaign-level decisions (e.g., design of the vehicle used throughout the campaign, destination demand assigned to each mission in the campaign), whereas the latter optimizes the detailed mission-level decisions (e.g., when to launch what from where to where). The framework is applied to a set of human lunar exploration campaign scenarios with uncertain in situ resource utilization performance as a case study. The main value of this work is its integration of the rapidly growing reinforcement learning research and the existing MILP-based space logistics methods through a hierarchical framework to handle the otherwise intractable complexity of space mission design under uncertainty. This unique framework is expected to be a critical steppingstone for the emerging research direction of artificial intelligence for space mission design.

Keidai Iiyama, Kento Tomita, Bhavi A. Jagatia et al.

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 selection utilize local terrain features such as slopes and roughness to judge the safety and priority of the landing point. However, when there are additional chances of observation and diverting in the future, these algorithms are not able to evaluate the safety of the decision itself to target the selected landing point considering the overall descent trajectory. In response to this challenge, we propose a reinforcement learning framework that optimizes a landing site selection strategy concurrently with a guidance and control strategy to the target landing site. The trained agent could evaluate and select landing sites with explicit consideration of the terrain features, quality of future observations, and control to achieve a safe and efficient landing trajectory at a system-level. The proposed framework was able to achieve 94.8 $\%$ of successful landing in highly challenging landing sites where over 80$\%$ of the area around the initial target lading point is hazardous, by effectively updating the target landing site and feedback control gain during descent.

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 identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs' reliability. In response to these limitations, this paper proposes an application of the Bayesian deep-learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by: (i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation; and (ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.