UAV Design Autonomy with Composite Structural Assembly
Unmanned Aerial Vehicles (UAVs) have immense potential. Designing Composite materials for UAV structural assembly of various kinds holds the key to sustainably applying UAV-based tasks across industry segments. in the design of their structures. However, the design and optimisation of composites have proven challenging, requiring a range of material property data and performance insights. Localised, small-scale Machine Learning (ML) is a way we have found to accelerate the closure of these gaps in the details of structural design and the optimisation of composite materials for UAV structures. The models trained on this engineering data shall allow for the autonomous design of UAVs.
– Shauray Kakade, Intern 2026
(Chancellor’s Gold Medalist at Vellore Institute of Technology)
Human Cognitive Functional Graph for AI
Working with large amounts of scientific literature and complex data comes with its own challenges. My research work at AATL presents a method that automatically finds, reads, organises, and learns from thousands of research papers on how the human brain works, extracting the key functions the brain performs and the order in which it performs them using AI models. A futuristic outlook is the possibility of a structured map tracing the brain’s full processing pathway, from the moment a stimulus is received through to a final decision or action.
–Avani Shivaramakrishnan, Intern 2026