RF-Source Seeking with Obstacle Avoidance using Real-time Modified Artificial Potential Fields in Unknown Environments

--Navigation of UA Vs in unknown environments with obstacles is essential for applications in disaster response and infrastructure monitoring. However, existing obstacle avoidance algorithms such as Artificial Potential Field (APF) are unable to generalize across environments with different obstacle configurations. Furthermore, the precise location of the final target may not be available in applications such search and rescue, in which case approaches such as RF source seeking can be used to align towards the target location. This paper proposes a real-time trajectory planning method, which involves real time adaptation of APF through a sampling-based approach. The proposed approach utilizes only the bearing angle of the target without its precise location, and adjusts the potential field parameters according to the environment with new obstacle configurations in real time. The main contributions of the article are i) RF source seeking algorithm to provide a bearing angle estimate using RF signal calculations based on antenna placement, and ii) modified APF for adaptable collision avoidance in changing environments, which are evaluated separately in the simulation software Gazebo, using ROS2 for communication. Simulation results show that the RF source-seeking algorithm achieves high accuracy, with an average angular error of just 1.48 degrees, and with this estimate, the proposed navigation algorithm improves the success rate of reaching the target by 46% and reduces the trajectory length by 1.2% compared to standard potential fields. The increasing use of drones in various applications has been facilitated by advancements in sensor technology, enabling better localization and obstacle detection methods. These technologies allow drones to effectively navigate through complex environments, avoiding obstacles in real time. The demand for autonomous drone navigation is growing in sectors like search and rescue [1], inspection of unknown areas [2], and other critical applications requiring drones to operate in unfamiliar and potentially hazardous environments. In these scenarios, drones must autonomously identify and locate targets, update environmental maps in real time, detect obstacles, and plan safe trajectories. The variability of these environments, such as changes in obstacle sizes, distances, and spatial constraints, poses a significant challenge to creating a unified navigation system that can adapt to such differing conditions.