Sensing-Aware Deep Reinforcement Learning With HCI-Based Human-in-the-Loop Feedback for Autonomous Nonlinear Drone Mobility Control

Abstract

This paper presents a novel approach for enhancing autonomous drone mobility control using deep reinforcement learning (DRL), primarily aimed at improving autonomous navigation in challenging environments. Our research tackles the significant issue of real-time obstacle avoidance, a critical aspect in drone control. This is achieved through the integration of sensing-aware nonlinear control mechanisms, facilitating advanced trajectory optimization. A notable contribution of our work is the incorporation of real-time human-in-the-loop feedback through human-computer interaction (HCI), which is crucial when pre-trained DRL models encounter environments they are not fully adapted to. Combining autonomous DRL control and HCI feedback equips our system with the flexibility to handle unforeseen scenarios effectively. Furthermore, the paper showcases a comprehensive software demonstration employing Unity 3D for visualization. This demonstration highlights the practical application of our sensing-aware nonlinear control and the HCI-based feedback system, using keyboard interfaces for real-time interaction. The accompanying demonstration video distinctly exhibits the ability of our proposed algorithm.

Keywords

• Autonomous mobility control
• drone
• reinforcement learning
• unity
• HCI
• human-in-the-loop