Motion planning system for unmanned aerial vehicles in dynamic three-dimensional space: a machine learning approach

Unmanned aerial vehicles (UAVs) have been highly enhanced in the last decade, targeting the deployment of UAV technology in different new applications in many fields. However, the motion control of the UAV during autonomous flight is still under development, especially in unstructured or dynamic environments. This paper presents an intelligent motion control framework using artificial potential field neural networks (APF-NNs) for UAVs in dynamic 3D environments. This approach is developed using potential field models for repulsive and attractive forces. Using the control commands of the APF-NN intelligent system, the UAV changes its planner position and altitude simultaneously to perform the path planning task. In other methods, the UAV only changes its planner positions for path planning task. The presented work is validated using simulations and field experiments. The proposed approach is outperforming existing fuzzy logic and classical potential field-based methods with a 75% and 86% reduction in computational time, 22% and 35% increase in inside success rate, 1.5% and 14.7% increase in the speed and 6.2% and 11% reduction in total traveled distance, respectively.