• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.45 no.1
• /
• pp.1-8
• /
• 2008

A two-wheel balancing vehicle, which helps people moving freely and fast, and is applied from inverted pendulum system, has been widely researched and developed, and some products are came into a market in actuality. Until now, the two-wheel balancing vehicles developed have chosen the general PID control method. In this paper, we propose a new control method to improve a control capacity for a two-wheeled balancing vehicle for human transportation. The proposed method is the fuzzy PD+I control that is one of the improved PID control, and it contains a 2input-1output fuzzy system. This fuzzy system processes signals from proportional and derivative controller, and the fuzzy output signal generates the final output by summing up integral signal. The non-linearity of the fuzzy system makes an optimal output control signal by changing weight of the proportional signal and the derivative signal in process of time. We have simulated the fuzzy PD+I control system and experimented by implementing the two-wheel balancing mobile robot to verify the advantages of the proposed fuzzy PD+I control method in comparison with general PID control. As the results of simulation and experimentation, the proposed fuzzy PD+I control method has better control performance than general PID in this system and improves it.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.174-179
• /
• 2003

Through the simultaneous localization and map building (SLAM) technique, a robot can create maps about its unknown environment while it continuously localizes its position. Grid maps and feature maps have been widely used for SLAM together with application of probability methods and POMDP (partially observed Markov decision process). But this approach based on grid maps suffers from enormous computational burden. Topological maps, however, have drawn more attention these days because they are compact, provide natural interfaces, and are easily applicable to path planning in comparison with grid maps. Some topological SLAM techniques like GVG (generalized Voronoi diagram) were introduced, but it enables the robot to decide only whether the current position is part of GVG branch or not in the GVG algorithm. In this paper, therefore, to overcome these problems, we present a method for updating a global topological map from the local topological maps. These local topological maps are created through a labeled Voronoi diagram algorithm from the local grid map built based on the sensor information at the current robot position. And the nodes of a local topological map can be utilized as the features of the environment because it is robust in light of visibility problem. The geometric information of the feature is applied to the extended Kalman filter and the SLAM in the indoor environment is accomplished. A series of simulations have been conducted using a two-wheeled mobile robot equipped with a laser scanner. It is shown that the proposed scheme can be applied relatively well.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.7
• /
• pp.1694-1704
• /
• 2015

In this paper, a robust trajectory tracking control method of a wheeled mobile robot is newly proposed combining the PDC and the ISMC. The PDC is a relatively simple and easy control method for nonlinear system compared to the other non-linear control methods. And the ISMC can have robust and stable control characteristics against model uncertainties and disturbances from the initial time by placing the states on the sliding plane with desired nominal dynamics. Therefore, the proposed PDC+ISMC trajectory tracking control method shows robust trajectory tracking performance in spite of external disturbance. The tracking performance of the proposed method is verified through simulations. Even though the disturbance increases, the proposed method keeps the performance of the PDC method when there is no disturbance. However, the PDC trajectory tracking control method has increasing tracking error unlike the proposed method when the disturbance increases.

• Structural Engineering and Mechanics
• /
• v.62 no.5
• /
• pp.619-629
• /
• 2017

This paper focuses on the study of complete dynamic modeling and maximum dynamic load carrying capacity computation of N-flexible links and N-flexible joints mobile manipulator undergoing large deformation. Nonlinear dynamic analysis relies on the Timoshenko theory of beams. In order to model the system completely and precisely, structural and joint flexibility, nonlinear strain-displacement relationship, payload, and non-holonomic constraints will be considered to. A finite element solution method based on mixed method is applied to model the shear deformation. This procedure is considerably more involved than displacement based element and shear deformation can be readily included without inducing the shear locking in the element. Another goal of this paper is to present a computational procedure for determination of the maximum dynamic load of geometrically nonlinear manipulators with structural and joint flexibility. An effective measure named as Moment-Height Stability (MHS) measure is applied to consider the dynamic stability of a wheeled mobile manipulator. Simulations are performed for mobile base manipulator with two flexible links and joints. The results represent that dynamic stability constraint is sensitive when calculating the maximum carrying load. Furthermore, by changing the trajectory of end effector, allowable load also changes. The effect of torsional spring parameter on the joint deformation is investigated in a parametric sensitivity study. The findings show that, by the increase of torsional stiffness, the behavior of system approaches to a system with rigid joints and allowable load of robot is also enhanced. A comparison is also made between the results obtained from small and large deformation models. Fluctuation range in obtained figures for angular displacement of links and end effector path is bigger for large deformation model. Experimental results are also provided to validate the theoretical model and these have good agreement with the simulated results.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.2
• /
• pp.140-146
• /
• 2013

In this paper, we describe a vision sensor-based driving algorithm for indoor automatic guided vehicles (AGVs) that facilitates a path tracking task using two mono cameras for navigation. One camera is mounted on vehicle to observe the environment and to detect markers in front of the vehicle. The other camera is attached so the view is perpendicular to the floor, which compensates for the distance between the wheels and markers. The angle and distance from the center of the two wheels to the center of marker are also obtained using these two cameras. We propose five movement patterns for AGVs to guarantee smooth performance during path tracking: starting, moving straight, pre-turning, left/right turning, and stopping. This driving algorithm based on two vision sensors gives greater flexibility to AGVs, including easy layout change, autonomy, and even economy. The algorithm was validated in an experiment using a two-wheeled mobile robot.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.2
• /
• pp.138-145
• /
• 2000

A identification algorithm that finds optimal fuzzy membership functions and rule base to fuzzy model isproposed and a fuzzy controller is designed to get more accurate position and velocity control of wheeled mobile robot. This procedure that is composed of three steps has its own unique process at each step. The elements of output term set are increased at first step and then the rule base is varied according to increase of the elements. The adjusted system is in competition with system which doesn't include any increased elements. The adjusted system will be removed if the system lost. Otherwise, the control system is replaced with the adjusted system. After finished regulation of output term set and rule base, searching for input membership functions is processed with constraints and fine tuning of output membership functions is done.