COMPARATIVE ANALYSIS OF SIMULATION PLATFORMS FOR UAV STABILIZATION WITH REINFORCEMENT LEARNING METHODS

Abstract

This paper presents an in-depth comparative analysis of four prominent simulation platforms commonly utilized for unmanned aerial vehicle (UAV) stabilization tasks involving reinforcement learning (RL): AirSim, Gazebo with RotorS, Flightmare, and Unity ML Agents. The evaluation is structured around five pivotal criteria that are essential for effective RL training in the context of UAV stabilization: the realism of physics simulation, the fidelity and variety of sensor emulation, the ease and depth of integration with RL frameworks, the capability to model atmospheric turbulence, and the degree of flexibility offered for environment customization. Each platform was systematically assessed in simulated scenarios reflecting real-world UAV stabilization challenges.

The findings reveal nuanced strengths and limitations across the platforms. Flightmare excels in physics realism and seamless RL integration, making it particularly suited for high-precision stabilization tasks in dynamic environments. However, its limited support for environment customization may constrain its broader applicability. AirSim emerges as a versatile choice, offering robust sensor simulation and a good balance between realism and configurability, positioning it well for general-purpose UAV training scenarios. Gazebo with RotorS demonstrates exceptional environment customization capabilities and modular architecture but faces integration complexities with modern RL toolkits. Unity ML Agents offers a user-friendly interface and fast prototyping benefits but falls short in simulating the complex aerodynamics necessary for advanced UAV stabilization.

This study emphasizes the importance of aligning simulation platform capabilities with the specific needs of UAV stabilization research and development. Moreover, it underscores the necessity of continued innovation to bridge the sim-to-real transfer gap that hinders the deployment of RL-trained UAV control systems in practical settings.