robust mobile robot path trajectory tracking based on in intelligence swarm-neural algorithm

robust mobile robot path trajectory tracking based on in intelligence swarm-neural algorithm

today, mobile robots are growing and developing applications and entering the real world. one of the most important issues related to mobile robots is robust path or routing tracking problem. we are always looking for optimal routes in terms of distance and time with minimum error in routing a mobile robot, so it understands and meets the environmental conditions and restrictions.

mobile robot routing tracking is one of the np-hard problems, because the complexity of this issue increases exponentially with the increase of constraints and space dimension. the main goal in robust trajectory tracking is to find the path between the starting point and the target point without hitting fixed and moving obstacles. also, finding the optimal route according to evaluation criteria such as route length, route time, energy consumption or less risk will increases the complexity of the robot's decision making.

in this paper, robust mobile robot path trajectory tracking is simulated with the d* lite algorithm along with the deep recurrent neural network (drnn) in the matlab platform. we use path recognition in robust and optimal way with observer to calculate and identify control errors based on the d* lite algorithm and tracking the targets of mobile robot without encountering obstacles and other disturbances with training in a rdnn. the simulation results represent the proposed method leads to optimize route tracking and cost reduction in a dynamic environment with uncertainty.