Purdue Orbital Deployment Assessment System (PODAS)

In the broader context of humanity’s future, the way space resources are managed today will shape the trajectory of our species. A failure to regulate and ensure the responsible use of space could lead to a future where space exploration is no longer viable. If managed sustainably, however, space will remain a frontier for innovation, cooperation, and the expansion of human capabilities, offering untapped opportunities for the future.

The sustainability of Low Earth Orbit (LEO) has emerged as a critical issue with the rapid expansion of commercial space activities, and effective management of space operations has become essential. However, there remains a significant gap in our fundamental understanding of the stochastic and dynamic nature of the LEO environment. To address this challenge, the PODAS project seeks to develop advanced mathematical models. First, we are developing novel complex stochastic models of the LEO environment. Second, we are advancing mechanism design, control and optimization methodology, to ensure equitable allocation and efficient utilization of space resources. These efforts are aimed at fostering sustainable practices in space operations and mitigating the long-term risks posed by congestion and resource overuse in LEO.

As companies race to exploit space resources, such as satellites for communications or even plans for asteroid mining, the impact of unchecked activity may have long-term consequences. Overcrowding in LEO could disrupt crucial services that society increasingly relies upon, including remote sensing, internet communications, and weather forecasting. The tragedy of the commons, a concept where shared resources are depleted due to individual self-interest, is a growing concern in space. LEO is already filled with satellites, space debris, and other assets, and as more private companies launch their ventures, the overutilization of this orbital space poses serious risks. The potential for collisions and the accumulation of debris could create a cascading effect, known as the Kessler Syndrome, which could hinder humanity’s ability to access space safely. Additionally, if LEO becomes a graveyard for obsolete satellites and debris, it could take centuries to clear the space. Sustainable practices, such as debris removal technologies, international space governance, and responsible satellite management, will be crucial in preserving LEO for future generations.

Our goals in addressing the sustainability of space, particularly in Low Earth Orbit (LEO), are twofold. First, we aim to create stochastic models by viewing space activities as complex stochastic interacting particle systems. This approach allows us to better understand the dynamic interactions between satellites, debris, and other assets in orbit. By modeling these elements as interacting particles subject to random forces and events, we can simulate and predict the long-term evolution of LEO environments. These models help us anticipate risks, such as collisions or the cascading debris effects of the Kessler Syndrome, and guide efforts to mitigate them before they reach critical levels.

Second, we are focused on developing mechanism design, control, and optimization methods that ensure the fair allocation and utilization of space resources. With the increasing commercialization of space, equitable access and the efficient management of finite orbital and radio resources are essential. Our work in mechanism design seeks to create systems that incentivize responsible behavior from space actors, such as satellite operators and private companies, while ensuring the shared benefits of space are distributed fairly. By applying control and optimization techniques, we aim to minimize resource wastage, mitigate the risk of overutilization, and create sustainable protocols that will allow space to be a viable domain for future generations.

Related Purdue Engineering Work

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Team


Team Leaders

Souvik Dhara
Assistant Professor

Research Interests

Phase transitions in complex networks.

Sustainability of LEO systems.

Algorithms and inference on networks.

Harsha Honnappa
Associate Professor

Research Interests

Multi-scale and multi-fidelity approximations of stochastic systems.

Stochastic optimal control.

Probabilistic machine learning with stochastic models.

Sustainability of LEO Systems.


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