A Novel Potential Field Controller for Use on Aerial Robots
This is an incremental improvement for drone navigation, addressing safety and efficiency in obstacle avoidance and target tracking.
The paper tackled the problem of enabling drones to track dynamic targets while avoiding obstacles by proposing an extended potential field controller (ePFC), which outperformed a traditional PFC with smoother paths and shorter settling times in simulations and lab experiments.
Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential uses in real world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller (PFC) with smoother tracking paths and shorter settling times. The proposed ePFC's stability is evaluated by Lyapunov approach, and its performance is simulated in a Matlab environment. Finally, the controller is implemented on an experimental platform in a laboratory environment which demonstrates the effectiveness of the controller.