Speaker
Description
The continuous growth of the orbital debris population has intensified the challenges associated with the sustainability of space activities, particularly in high-density orbital environments and under increasing competition for capacity. Although several debris mitigation and active removal approaches have been proposed, the operational limitations of such missions impose the need for strategic decisions regarding which objects should be prioritized, in what sequence, and under which criteria. Existing approaches often consider isolated factors or rely on static models, failing to adequately capture the dynamic, systemic, and interdependent nature of the orbital environment.
This work proposes an intelligent multi-criteria prioritization system to support decision-making in orbital debris removal missions, explicitly considering capacity and operational cost constraints. The approach integrates technical variables such as collision probability (Pc), altitude and orbital regime (LEO, MEO, GEO), local object density, orbital parameters (inclination, eccentricity), orbital lifetime, area-to-mass ratio, and the potential contribution to cascading fragmentation events. Additionally, mission feasibility factors are considered, including estimated energy cost (delta-v), orbital accessibility, and the possibility of chaining multiple removals within a single trajectory.
The system aims not only to select targets but also to define an optimized removal sequence over time, formulating the problem as a constrained multi-objective optimization, in which the goal is to maximize global risk reduction while minimizing operational costs and capacity limitations. This approach enables the treatment of debris removal as a dynamic resource allocation problem within an evolving environment.
As a distinguishing feature, the proposal incorporates an adaptive mechanism based on artificial intelligence, capable of processing data from public orbital catalogs, identifying temporal and spatial patterns, and continuously adjusting removal priorities. The system may employ hybrid strategies, combining heuristic models with adaptive learning, enabling near real-time responses to changes in the orbital environment and to unexpected events.
Furthermore, the system integrates the identification of object origin, enabling the association between debris and their respective launching states based on available data. This functionality expands the scope of the proposal by incorporating elements of governance and international responsibility, offering potential support for prioritization mechanisms that consider not only technical criteria but also regulatory and cooperative dimensions.
The approach considers different mitigation strategies, such as controlled re-entry or orbital relocation, emphasizing that redistributing debris does not eliminate systemic risk, but rather shifts it within the orbital environment. In this context, the system also incorporates the assessment of atmospheric re-entry risk, considering variables such as mass, ballistic coefficient, material composition, and fragment survivability, allowing the estimation of potential risks to the Earth's surface and integrating both orbital and terrestrial safety dimensions into the decision-making process.
By addressing orbital debris removal as a decision-making problem under constraints and systemic impact, the proposal encourages a shift in perspective: from isolated interventions to a holistic view of the space environment as a shared and interdependent system. In this context, sustainability is not limited to the immediate reduction of risks, but involves the development of strategies capable of continuously preserving the conditions that make space usable for present and future generations.
More than a purely technical challenge, orbital debris management emerges as a collective responsibility, in which local decisions have global consequences, affecting not only specific missions but the entire ecosystem of space activities and their impacts on Earth. The integration of artificial intelligence, technical criteria, and governance elements suggests that technological advancement can — and should — evolve alongside principles of sustainability and responsibility.
Thus, the proposal reinforces the idea that the development of innovative solutions does not need to occur at the expense of environmental balance, but can instead be guided by it. By promoting an integrated, adaptive, and impact-oriented approach, this work aims to contribute to a model of technological evolution that recognizes space as a common heritage of humanity, whose preservation is essential for the future of space activities and for the continuity of their benefits on Earth.
| Which section would you like to submit your abstract to? | Session 4: “How space debris mitigation can adapt to the space environment?” |
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