Path planning for remotely controlled UAVs using Gaussian process filter
Jaehyun Yoo, H. Jin Kim, Karl Henrik Johansson · 2017
Most of the recent results in control of unmanned aerial vehicles (UAVs) have focused on motion stability and navigation in well-structured environments, without considering communication delay influences. In order to deal with time delays and packet losses in networked UAVs, this paper suggests a machine learning based Gaussian process (GP) filter for a path planning problem. The developed GP filter estimates the UAV states accurately given delayed observation by learning the pattern of network-induced effects on UAV maneuvers. We validate that the GP filter produces the lower error rate than Kalman filter by analyzing error covariances. The proposed algorithm is evaluated on a collaborative trajectory tracking task for two networked-UAVs and the better control performance is achieved.