Improved line of sight robot tracking toward a moving target

Abstract

In this paper, the line of sight (LOS) guidance law is improved to implement tracking toward a moving target. In the presence of sensor noise, an optimal information fusion Kalman filter weighted by scalars is utilized for two-sensor information fusing, improving the trajectory tracking precision. Under the communication delay, n-step ahead Kalman predictor compensates for communication delay and provides LOS guidance law with more accurate target estimates. The results of the simulation demonstrate the feasibility and effectiveness of the proposed control strategy.

An intelligent fast controller for autonomous wheeled robot path navigation in challenging environments

“…In a Delaunay network, the shortest way is fixed by the Dijkstra algorithm, and then a genetic algorithm (GA)-based artificial potential field (APF) method is used for fixed-wing unmanned aerial vehicles (UAVs) (Qu et al , 2017). Using multiple sensors information, the line of sight governing law improved by the Kalman filter function for robots in a dynamically challenging environment (Feng et al , 2018). Multiple goal-seeking path navigation algorithms are used for a mobile robot with the presence of dynamic obstacles (An et al , 2018).…”

Obstacle-avoiding intelligent algorithm for quad wheel robot path navigation

“…Fixed wing UAVs with joint heuristic processes have been explained in Qu et al (2018). Moving obstacle tracking by Extended Kalman Filter (EKF) has been presented in Feng et al (2018). An algorithm for path navigating with adaptive neural network has been introduced by Ding et al (2017).…”