Grey wolf optimization based sense and avoid algorithm in a Bayesian framework for multiple UAV path planning in an uncertain environment

Unmanned Air Vehicles (UAVs), which have been popular in the military context, have recently attracted attention of many researchers because of their potential civilian applications. However, before UAVs can fly in civilian airspace, they need to be able to navigate safely to their goal while maintaining separation with other manned and unmanned aircraft during the transit. Algorithms for autonomous navigation of UAVs require access to accurate information about the state of the environment in order to perform well. However, this information is often uncertain and dynamically changing. In this paper, a Grey Wolf Optimization (GWO) based algorithm is proposed to find the optimal UAV trajectory in presence of moving obstacles, referred to as Intruder Aircraft (IAs), with unknown trajectories. The solution uses an efficient Bayesian formalism with a notion of cell weighting based on Distance Based Value Function (DBVF). The assumption is that the UAV is equipped with the Automatic Dependent Surveillance-Broadcast (ADS-B) and is provided with the position of IAs either via the ADS-B or ground-based radar. However, future trajectories of the IAs are unknown to the UAV. The proposed method is verified using simulations performed on multiple scenarios. The results demonstrate the effectiveness of the proposed method in solving the trajectory planning problem of the UAVs.