Collision-free Path Planning for UAVs using Efficient Artificial Potential Field Algorithm

Unmanned Aerial Vehicles (UAVs), a new emerging form of Internet of Things (IoT), is a promising technology to be widely used in both civil and military applications. On the fly, the UAVs need to find an efficient and safe path by avoiding both static and dynamic obstacles to carry out any mission successfully. The Artificial Potential Field (APF) algorithm is one of the conventional catalysts in UAV path planning. However, APF-aided UAVs can be easily trapped into a local minimum solution before reaching the destination. Therefore, this paper proposes an efficient APF algorithm for Collision-free Path Planning (eAPF-CPP) in UAVs. In eAPF-CPP, the attractive and repulsive potentials evaluate the quadratic distance to the destination and the obstacle respectively. The evaluation aids the UAV to select the optimal path in navigation. The eAPF-CPP mechanism is simulated in the Software-In-The-Loop (SITL) setup, and the experimental results show that the eAPF-CPP mechanism utilizes an average of 24.4 seconds to track a safe path and has a lower collision rate of 8.56% compared with Artifical Potential Field Approach (APFA).