A Review of Control Algorithm for Autonomous Guided Vehicle

Abstract

The autonomous guided vehicle is a efficient and<br />effective platform for control system. Their non-linear nature helps<br />in analysing the control algorithms more efficiently and effectively.<br />The main objective of path planning is to find the optimal and<br />shortest path avoiding the time complexity so environment can be<br />modelled completely for vehicle. The paper includes explanation<br />of different systems together with numerous algorithms have been<br />discussed with advantages and disadvantages for example: Fuzzy<br />control, Neural Control, Back-stepping control, Adaptive control,<br />Sliding mode control and PID control and linear quadratic regulator.<br />The conclusion includes the hybrid system integration based<br />on the advantages and disadvantages presented in this paper.

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

“…Later on, numerous controllers have been developed for dealing with the non-linear characteristics of autonomous vehicles. Different controllers has been designed for autonomous guided vehicles, e.g., PID controller [105], sliding mode controller [106], linear quadratic regulator [107], fuzzy logic controller [108], backstepping controller [109], adaptive control [110], and pure pursuit controller [77]. Some related work from the literature is referenced below: Thaer et al [111] studied the robotic arm control parameters with numerical solutions involved with the help of the Runge-Kutta method.…”

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

“…The rapidly increasing fleet of UAVs, along with the widening sphere of their utility, therefore presents a serious challenge for the designers to formulate unique optimal control strategies. However, technological advancements in the aviation sector [4][5][6][7] and ground control vehicles [8][9][10][11][12][13][14][15][16][17][18][19] paved the way for the development of hi-fidelity systems. These UAVs help researchers by providing means to collect multi-spectral information with limited resources and data collection times which is critical for time sensitive dynamic data [20].…”

An adaptive neural control methodology design for dynamics mobile robot

“…In the last decade, many commercial robots have been utilized in real-world applications such as robotic vacuum cleaners, lawnmowers, weather detection, mining envioronment, educational applications, entertainment, and military equipment [1], [2] therefore, to improve the mobile robot path planning and to track various trajectories that have several challenges, some design stages should be addressed such as simultaneous localization and mapping [3], [4], path-tracking and mobile robot platform object detection [5]- [7], and the static mobile surveillance systems convergence are proposed in [8], [9]. In this context, this work focuses on mobile robot path planning and how to generate the shortest path of the mobile robot platform in the obstacles environment between (the starting position and the target position) in order to avoid the collision, so many researchers use various optimization path-planning algorithms that they are tried to solve path-planning problems.…”