• The Journal of Korea Robotics Society
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• v.5 no.4
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• pp.349-358
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• 2010

We presents a dynamic modeling of 4-wheel 2-DOF. WMR. The classic dynamic model utilizes a greatly simplified wheel motion representation and using of a simplified dynamic model confronts with a problem for accurate position control of wheeled mobile robot. In this paper, we treats the dynamic model for describes relationship between the wheel actuator force/torque and WMR motion through the use of Newton's equilibrium laws. To calculate the WMR position in real time, we introduced the Dead-Reckoning algorithms and the simulation result show that the proposed dynamic model is useful. We can be easily extend the proposed WMR model to mobile robot of similar type and this type of methodology is useful to analyze, design and control any kinds of rolling robots.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.711-719
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• 2009

This paper presents a design scheme of torque control for wheeled mobile robot(WMR) to asymptotically track the target reference trajectory. By considering the kinematic model of WMR, trajectory tracking control generates the desired tracking trajectory, which is transformed into the command velocity vector for the real WMR to track the target reference trajectory. The dynamic equation of the state error between the target reference trajectory and the desired tracking trajectory is represented by Takagi-Sugeno fuzzy model, and this model is used as the reference model for the real mobile robot error dynamics to follow. The control parameters are updated by adaptive laws that are designed for the error states of the real WMR to asymptotically follow the states of reference error model for the desired tracking trajectory. The proposed control is applied to a typical wheeled mobile robot and simulation studies are carried out to verify the validity and effectiveness of the control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1259-1263
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• 2004

This paper describes position estimation algorithm using neural network for the navigation of the vision-based wheeled mobile robot (WMR) in a corridor with taking ceiling lamps as landmark. From images of a corridor the lamp's line on the ceiling in corridor has a specific slope to the lateral position of the WMR. The vanishing point produced by the lamp's line also has a specific position to the orientation of WMR. The ceiling lamps have a limited size and shape like a circle in image. Simple image processing algorithms are used to extract lamps from the corridor image. Then the lamp's line and vanishing point's position are defined and calculated at known position of WMR in a corridor. To estimate the lateral position and orientation of WMR from an image, the relationship between the position of WMR and the features of ceiling lamps have to be defined. But it is hard because of nonlinearity. Therefore, data set between position of WMR and features of lamps are configured. Neural network are composed and learned with data set. Back propagation algorithm(BPN) is used for learning. And it is applied in navigation of WMR in a corridor.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1682-1692
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• 2003

This paper proposes an adaptive controller for partially known system and applies to a two-wheeled Welding Mobile Robot (WMR) to track a reference welding path at a constant velocity of the welding point. To design the tracking controller, the errors from WMR to steel wall is defined, and the controller is designed to drive the errors to zero as fast as desired. Additionally, a scheme of error measurement is implemented on the WMR to meet the need of the controller. In this paper, the system moments of inertia are considered to be partially unknown parameters which are estimated using update laws in adaptive control scheme. The simulations and experiments on a welding mobile robot show the effectiveness of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.191-196
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• 2003

This paper presents a method to design an adaptive controller for the kinematic model of a two-wheeled welding mobile robot (WMR) with unknown parameters. We propose a nonlinear controller based on the Lyapunov function to enhance the tracking properties of the WMR. The WMR can track any smooth curved welding path at a constant velocity of the welding point. The system has three degrees of freedom including two wheels and one torch slider. Torch slider motion is used for fast tracking. To design the tracking performance, the errors from WMR to steel wall is defined, and the controller is designed to drive the errors to zero as fast as possible. The effectiveness of the proposed controller is shown through simulation results.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.173-177
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• 2002

This paper describes the algorithm for WMR (Wheeled Mobile Robot) to follow the preceding object using stereo vision. The center point of object existing in each image is calculate. From the distance and variation of two center points, we calculates the distance from WMR to preceding object and the relative velocity of WMR. And we use the Lyapunov theory to design controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.49-53
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• 2011

Path following of the mobile robot is one research hot for the mobile robot navigation. For the control system of the wheeled mobile robot(WMR) being in nonhonolomic system and the complex relations among the control parameters, it is difficult to solve the problem based on traditional mathematics model. In this paper, we presents a simple and effective way of implementing an adaptive following controller based on the PID for mobile robot path following. The method uses a non-linear model of mobile robot kinematics and thus allows an accurate prediction of the future trajectories. The proposed controller has a parallel structure that consists of PID controller with a fixed gain. The control law is constructed on the basis of Lyapunov stability theory. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity and orientation tracking control of the nonholonomic WMR. The simulation results of wheel type mobile robot platform are given to show the effectiveness of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.577-582
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• 2004

This paper describes position estimation algorithm using neural network for the navigation of the vision-based Wheeled Mobile Robot (WMR) in a corridor with taking ceiling lamps as landmark. From images of a corridor the lamp's line on the ceiling in corridor has a specific slope to the lateral position of the WMR. The vanishing point produced by the lamp's line also has a specific position to the orientation of WMR. The ceiling lamps has a limited size and shape like a circle in image. Simple image processing algorithms are used to extract lamps from the corridor image. Then the lamp's line and vanishing point's position are defined and calculated at known position of WMR in a corridor To estimate the lateral position and orientation of WMR from an image, the relationship between the position of WMR and the features of ceiling lamps have to be defined. Data set between position of WMR and features of lamps are configured. Neural network are composed and teamed with data set. Back propagation algorithm(BPN) is used for learning. And it is applied in navigation of WMR in a corridor.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.145-153
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• 2000

This paper discusses the dynamic modeling and robust control development for a differentially steered mobile robot subject to wheel slip according to high load. Consideration of wheel slip is crucial for high load applications such as construction automation tasks because wheel slip acts as a severe disturbance to the system. It is shown that the uncertainty terms due to the wheel slip satisfy the matching condition for the sliding mode control design. From the full dynamic model of the mobile robot, a reduced ideal model is extracted to facilitate the control design. The sliding mode control method ensures the dynamic tracking performance for such a mobile robot. Numerical simulation shows the promise of the developed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1241-1248
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• 2004

A controller of wheeled mobile robot(WMR) based on Lyapunov theory is designed and a Fuzzy-Neural Network algorithm is applied to this system to adjust controller gain. In conventional controller of WMR that adopts fixed controller gain, controller can not pursuit trajectory perfectly when initial condition of system is changed. Moreover, acquisition of optimal value of controller gain due to variation of initial condition is not easy because it can be get through lots of try and error process. To solve such problem, a Fuzzy-Neural Network algorithm is proposed. The Fuzzy logic adjusts gains to act up to position error and position error rate. And, the Neural Network algorithm optimizes gains according to initial position and initial direction. Computer simulation shows that the proposed Fuzzy-Neural Network controller is effective.