AIRSHIP

Project Overview

The AIRSHIP project focuses on developing cutting-edge autonomous systems for Unmanned WIG (Wing-in-Ground) Vehicles. These innovative aircraft operate in the ground effect zone, offering unique advantages in terms of fuel efficiency and payload capacity for maritime and coastal applications.

As Lead Research Scientist for RL-based autonomous navigation systems, I'm developing a novel modular flight dynamics simulator, used to derive advanced Guidance, Navigation, and Control (GNC) strategies that leverage both optimal control and reinforcement learning policies to enable fully autonomous operation in complex flight control scenarios.

Technical Innovation

🛩️ Advanced GNC Systems

Development of sophisticated guidance, navigation, and control algorithms specifically designed for WIG vehicle dynamics and ground effect phenomena.

🤖 RL-based Autonomous Control

Implementation of deep reinforcement learning policies for autonomous maneuvering with enhanced speed, flexibility, and energy efficiency.

My Research Contributions

Reinforcement Learning for WIG Vehicles

Modular flight dynamics simulator: Developed a novel modular flight dynamics simulator that operates both in MATLAB and Python, enabling seamless integration of RL algorithms for WIG vehicle dynamics.
RL-based control strategies: Designed and implemented reinforcement learning-based control strategies for autonomous flight in ground effect.
Traditional control integration: Integrated traditional control techniques like PID, LQR and MPPI as baseline comparisons for RL policies.

Project Resources

Official Project Website Project Objectives