Sirvi Autor "Paul, Aditya Savio" järgi

Nüüd näidatakse 1 - 2 2

Autonomous motion planning for spacecrafts near small solar system bodies: simultaneously refining the gravitational field model and re-planning gravity dependant maneuvers
(Tartu Ülikool, 2020) Paul, Aditya Savio; Otte, MichaelW.; Allik, Viljo; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
Small solar system bodies can be better studied while orbiting in their vicinity. However, orbital motion around such bodies is challenging due to their irregular and weaker gravity as compared to larger bodies. Moreover, a-priori paths developed by earth-based measurements tend to generate monolithic trajectories. Dynamic path planning in space has the potential to improve the study of small solar system bodies. Fine-grained motion plans require detailed knowledge of the gravitational forces, that can be measured in the sphere of influence. The gravity models can be analysed for mass and material distribution across the body. We propose a method for autonomous motion planning around small solar system bodies that simultaneously measures and refines the gravitational model. The trajectories are replanned considering the updated model to perform stable orbital maneuvers eventually providing a high fidelity gravity model. The research shall enable the spacecraft to perform autonomous maneuvers, design landing strategies and scout for in-situ resources.
Optimal agent positioning for dynamic event monitoring and analysis.
(Tartu Ülikool, 2024) Naghma Afreen; Paul, Aditya Savio; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
Events like natural hazards and celestial occurrences are dynamic in nature, they are unpredictable and change instantaneously. In order to study events better, it is essential to develop methods for better understanding. However, observing such events is challenging due to their changing nature compared to observation of events that do not change with time. Moreover, traditional camera systems developed for observing static events tend to generate results with less accuracy and information loss. Dynamic event observation requires dynamic setup of camera systems to improve the study of such phenomena. We propose a method for optimizing camera configurations for observing dynamic events. The observation space is sampled using a volumetric sampler. The cameras are iterated over these samples and the event is captured; an optimal solution then evaluated using a scoring criteria. The scoring function assists in analyzing the optimal configuration of camera setup for observing the desired event. The research aims to develop and improve observational techniques for dynamic events.