Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Intelligent control of miniature holonomic vertical take-off and landing robot

This paper discusses the development of a fuzzy based controller for miniaturized unmanned aerial vehicle (UAV).This controller is designed to control the center-of-gravity (CoG) in a new configuration of coaxial miniaturized flying robot (MFR). The idea is to shift the CoG by controlling two pendulums located in perpendicular directions; each pendulum ends with a small mass. A key feature of this work is that the control algorithm represents the original nonlinear function that describes the dynamics of the proposed system. The controller model incorporates two cascaded subsystems: PD and PI fuzzy logic controllers. These two controllers regulate the attitude and the position of the flying robot, respectively. A model of the proposed controllers has been developed and evaluated in terms of stability and maneuverability. The results show that the presented control system can be used efficiently for the MFR applications

Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles

Trajectory tracking is an essential capability of robotics operation in industrial automation. In this article, an artificial neural controller is proposed to tackle trajectory-tracking problem of an autonomous ground vehicle (AGV). The controller is implemented based on fractional order proportional integral derivative (FOPID) control that was already designed in an earlier work. A non-holonomic model type of AGV is analysed and presented. The model includes the kinematic, dynamic characteristics and the actuation system of the VGA. The artificial neural controller consists of two artificial neural networks (ANNs) that are designed to control the inputs of the AGV. In order to train the two artificial neural networks, Levenberg-Marquardt (LM) algorithm was used to obtain the parameters of the ANNs. The validation of the proposed controller has been verified through a given reference trajectory. The obtained results show a considerable improvement in term of minimising trajectory tracking error over the FOPID controller

Lateral guidance control of a low-speed vehicle

This thesis examines the lateral guidance control of a low-speed vehicle. Several topics are studied in detail: (1) vehicle error-state model for lateral guidance based on Ackerman steering and (2) lateral guidance control of a low-speed vehicle using fuzzy logic. Independently written research papers address each topic. The first paper presents a second order error-state kinematic model based on Ackerman steering appropriate for studying the lateral guidance control of low-speed vehicles traversing on roads of constant curvature. Lateral guidance control of vehicles is of great interest to the Advanced Vehicle Control Systems (AVCS) Division of the Intelligent Transportation System (ITS) community. Both linear and nonlinear models are derived in detail. The error states considered are the vehicle\u27s lateral error and heading error measured with respect to the instantaneous road centerline tangent. In addition to the derivation, both simulation and experimental results are presented with very good correspondence being achieved. The second paper investigates the performance of several different controllers used to perform lateral guidance control of a low-speed vehicle described as a linear nonminimum-phase error-state bicycle model based on Ackerman steering. Both a conventional type I proportional-integral (PI) controller and a fuzzy logic controller (FLC) are considered. The PI controller is designed using standard techniques and the two-level FLC / PI controller adjusts both proportional and integral feedback control gains around the baseline values based on heuristics and the current conditions as measured by the lateral error. Time-based simulations using MATLAB / SIMULINK permit a comparison between both controllers for several different simulation scenarios of interest. Primary performance metrics considered were percent overshoot and settling time in response to a step input. In general, the two-level FLC / PI controller performed better; 6 % reduction in overshoot and 21 % reduction in settling time

Behavior-based Fuzzy Control For A Mobile Robot With Non-holonomic Constraints

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2005Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2005Bu çalışmada robotik alanında yeni yaklaşımlar olan davranış temelli robotik ve bulanık mantık konuları gerçek zamanda mobil robot uygulamaları bakımından incelenmiş, dört ilerlemeli, dört yönelmeli bir mobil robot için Engelden Sakın , Hedefe Git , Duvarı İzle , Yola Teğet İlerle , Avare Gez davranışları oluşturulmuştur. Bu davranışların içinden Engelden Sakın , Hedefe Git ve Duvarı İzle davranışları için sonar sensör matematik modelleri oluşturulmuş ve bu davranışların yapısında bulanık mantık yaklaşımı kullanılmıştır. Mobil robot, kinetik ve dinamik olarak holonomik olmayan kısıtları kullanılarak modellenmiştir ve simülasyon sırasında mobil robotun pozisyonu, tekerlek ve robot yönelimleri, tekerlek ve robot hızları, tekerlek torkları gibi parametreler izlenebilmektedir. Davranışlar da, simülasyon ortamında kazanımları, bulanık mantık işleme yapıları, gerçek zaman uygulanabilirliği ve davranışların koordine edilmeleri bakımından incelenmiştir. Bu çalışma gerçek bir robotta yapılacak deneyler için temel teşkil etmektedir.In this study, the new approaches to the robotics subject, behavior-based robotics and fuzzy logic control are investigated for the real-time applications of mobile robots, Avoid Obstacle , Move to Goal , Wall Following , Head-on , Wander behaviors are built up for a four-wheel driven and four-wheel steered mobile robot. Sonar sensor mathematical models are formed for Avoid Obstacle , Move to Goal and Wall Following behaviors and fuzzy logic concepts are used in the structure of these behaviors. The mobile robot is modelled kinematically and dynamically considering the non-holonomic constraints. The posture and speed of the robot and the configurations, speeds and torques of the wheels can be obtained from the simulation. The behaviors are investigated regarding their gains, fuzzy inference structures, real-time applicabilities and thein coordination. This study constitutes basis for the experiments on a real mobile robot.Yüksek LisansM.Sc

Intelligent Adaptive Motion Control for Ground Wheeled Vehicles

In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies

Control structure for a car-like robot using artificial neural networks and genetic algorithms

The idea of improving human’s life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm “Neuroevolution of Augmented Topologies” and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison workCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQNão te