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Intelligent systems with state sensing and awareness capabilities

Students
Ahmad Amer
Airin Dutta
Peiyuan Zhou

This research thrust addresses the development and exploration of novel data-driven stochastic modeling and statistical learning approaches for enabling intelligent self-aware fly-by-feel aerial vehicles of the future that can (i) sense their surrounding environment, operating and structural states, (ii) model and interpret heterogeneous multi-modal sensing information, (iii) determine their actual operating state and structural health condition in highly-dynamic rapidly-evolving environments, and (iv) can self-assess the correctness of state estimation methods.

Projects

Data-driven statistical learning framework towards future state-aware VTOL aircraft
The operational success of Urban air mobility (UAM), enabled by autonomous electric Vertical Take-Off and Landing (eVTOL) aircraft requires real time safety assurance and complete autonomy. According to a technical report by Uber Elevate, the safety level in air-taxi aviation needs to improve from 1.2 to 0.3 fatalities per 100 million passenger miles through innovation with large amounts of data from real-world operations after the first-generation VTOLs are in production. To achieve that, development of a real-time system-health awareness and state sensing framework is in progress.

Formal verification of stochastic state awareness for dynamic data-driven aerospace systems
Postulation of novel formal verification framework for stochastic state awareness of intelligent aerial vehicles. This is a joint effort with Prof. Carlos Varela from the Computer Science Department at RPI.