Droneulator: A Portable UAV Simulator for Agricultural Workflows with RotorPy and Godot 4
For agricultural UAV researchers, Droneulator provides a unified simulator that combines realistic dynamics, rendering, and middleware, reducing the need for multiple simulation environments.
Droneulator integrates RotorPy and Godot 4 into a portable UAV simulator supporting PX4-based control, WebSocket commands, and Zenoh-based ROS 2-compatible pipelines. It demonstrates low-latency sensing, collision-free planning, and stable RL training across three agricultural workflows.
Agricultural UAV research requires simulators that integrate realistic 3D scenes, high-fidelity vehicle dynamics, and robotics middleware, while remaining practical to deploy across heterogeneous development machines. We present Droneulator, a portable UAV simulator architecture that combines RotorPy for multirotor dynamics with Godot 4 for rendering and sensor generation. Droneulator exposes both PX4-based control and a lightweight WebSocket command path, and publishes synchronised visual and state streams through a Zenoh-based ROS~2-compatible pipeline. This integration enables a single stack to support inspection-oriented data capture, ROS~2/PX4 local planning, and reinforcement learning experiments without modifying the simulator infrastructure. We present quantified validation of the current system across three agricultural UAV workflows: tree-scale image collection for 3D reconstruction with COLMAP, local planning around canopy obstacles using EGO-Planner, and closed-loop reinforcement learning through a custom Gymnasium environment. In the reported setup, the results show that the simulator can sustain low-latency sensing, support reconstruction-oriented data collection under varying capture density, execute collision-free local planning around canopy obstacles, and support stable depth-sensing-based policy training for obstacle-aware navigation. Together, these results show the potential of Droneulator for agricultural UAV inspection, planning, and learning within one deployable stack.