Fatimah Alahmed

Autonomous mobile robots need reliable path planning and local execution. Plenty of tools exist to evaluate either global planners or controllers. The open-source automated frameworks built for Robot Operating System 2 (ROS 2) do not measure computational cost alongside navigational reliability or report the statistics for multi-criteria decision making. The choice falls back on manual comparison, and that becomes harder as the environment changes. ROSNavBench is an open-source framework that automates this evaluation for global planners and controllers in ROS 2. It scores efficiency, reliability, and adaptability over a range of scenarios, and thus a user can pick the planner and controller that fit the job. Runs are automated and repeated, which tightens the statistics and takes the manual work out of the loop. We tested the framework across simulations and hardware. Navigation times in the two domains correlate at $r=0.97$, but a gap remains, and its width shifts from one trial to the next.

Visual localization has shown promise for accurate Unmanned Aerial Vehicle (UAV) navigation in GNSS-denied environments due to its high spatial resolution. However, performance declines in low-light or texture-scarce conditions and incurs high computational costs. In contrast, non-visual sensors offer a lightweight, low-complexity alternative for localization under such conditions. This work introduces CLAK (CNN-LSTM-Attention-KAN), a deep learning framework that estimates global UAV positions (latitude, longitude, and elevation) using non-visual sensors such as LiDAR, barometric altitude, and Inertial Measurement Unit (IMU). CLAK combines Convolutional Neural Networks (CNNs) for spatial encoding, Long Short-Term Memory (LSTM) layers for temporal modeling, attention for feature prioritization, and Kolmogorov-Arnold Networks (KANs) for flexible nonlinear regression. The model is trained on synthetic UAV flight data generated using a ROS2 simulation framework that integrates Gazebo for 3D environment simulation and PX4 for autopilot control, with QGroundControl (QGC) managing mission planning and monitoring. Elevation data is extracted from a Digital Elevation Map (DEM) of the Taif region in Saudi Arabia, while ground truth positions are derived from simulated onboard Global Positioning System (GPS) outputs. Simulation results across different flight trajectories show CLAK can achieve up to 78.35% Mean Absolute Error (MAE) and 75.40% Root Mean Squared Error (RMSE) reduction compared to the LSTM baseline, while maintaining an average coefficient of determination ( $$R^2$$ R 2 ) of 0.998. These results demonstrate the scalability and precision of CLAK for UAV localization in GNSS-denied conditions.