Publications

Reinforcement learning (RL) may enable fixedwing unmanned aerial vehicle (UAV) guidance to achieve more agile and complex objectives than typical methods. However, RL has yet struggled to achieve even minimal success on this problem; fixed-wing flight with RL-based guidance has only been demonstrated in literature with reduced state and/or action spaces. In order to achieve full 6-DOF RL-based guidance, this study begins training with imitation learning from classical guidance, a method known as warm-staring (WS), before further training using Proximal Policy Optimization (PPO). We show that warm starting is critical to successful RL performance on this problem. PPO alone achieved a 2% success rate in our experiments. Warm-starting alone achieved 32% success. Warm-starting plus PPO achieved 57% success over all policies, with 40% of policies achieving 94% success.

Abstract not provided.

This paper presents the Symbolic Linear Covariance Analysis Tool (SLiC), a Python framework capable of simplifying the construction, verification, and analysis of aerospace systems using linear covariance analysis techniques. The framework leverages open-source libraries to enable symbolic manipulation and object-oriented abstraction to remove many of the barriers to linear covariance analysis when compared to other methods. The benefits of linear covariance analysis with Monte Carlo verification are addressed and the framework design is described. The framework is validated against existing literature results and demonstrated for a sample aerospace use case of a hypersonic entry system.

Abstract not provided.