Monitoring space traffic

MIT PhD student Sydney Dolan is at the forefront of enhancing satellite safety in space through innovative collision-avoidance algorithms. Leveraging cutting-edge techniques like reinforcement learning, game theory, and optimal control, Dolan is developing systems that represent the space environment as an abstract graph. This approach enables more effective strategic decisions regarding satellite movements, helping to navigate the increasingly crowded orbital landscape.

As the number of satellites increases, the risk of collisions has become a pressing concern. Dolan’s work aims to address this challenge by creating algorithms that can predict and avoid potential crashes, ensuring the safety and longevity of vital space missions. By using reinforcement learning, the algorithms can learn from past data and improve their responses to ever-changing conditions in space. Game theory principles help model the interactions between multiple satellites, allowing for coordinated actions that minimize the risk of collision. Optimal control techniques ensure that the movements of satellites are efficient and responsive to unforeseen circumstances.

Dolan’s efforts are a significant step towards safer space operations, essential for the future of satellite communication, Earth observation, and exploratory missions. As her research continues to evolve, it could lead to practical applications that benefit satellite operators and contribute to the sustainability of outer space.

With an ambitious approach combining advanced mathematics, computer science, and aerospace engineering, Dolan embodies the innovative spirit of MIT, pushing the boundaries of what is possible in managing space traffic.

For more details on her groundbreaking research, visit MIT News: https://news.mit.edu/2025/monitoring-space-traffic-sydney-dolan-0108

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https://news.mit.edu/2025/monitoring-space-traffic-sydney-dolan-0108