Abstract: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.
| Subjects: | Robotics (cs.RO) |
| Cite as: | arXiv:2605.23386 [cs.RO] |
| (or arXiv:2605.23386v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23386 arXiv-issued DOI via DataCite (pending registration) |
From: Jacob Swindell [view email]
[v1]
Fri, 22 May 2026 08:58:29 UTC (3,630 KB)
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