2,028 research outputs found

    Position control for wheeled mobile robots using a fuzzy logic controller

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    Author name used in this publication: P. K. S. TamAuthor name used in this publication: F. H. F. LeungAuthor name used in this publication: T. H. LeeVersion of RecordPublishe

    Development Of Mobile Robot Control Algorithm based On The Fuzzification Of The Local Terrain Map

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    Based on a review and analysis of the literature for controlling a small light mobile robot based on fuzzy logic, an algorithm was developed based on the fuzzification of the local area and on determining the danger of traffic. The obtained algorithm can be used in the development of control systems for wheeled robots for various purposes, in particular robots designed to operate in confined spaces

    Speed control for leader-follower robot formation using fuzzy system and supervised machine learning

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    Mobile robots are endeavoring toward full autonomy. To that end, wheeled mobile robots have to function under non-holonomic constraints and uncertainty derived by feedback sensors and/or internal dynamics. Speed control is one of the main and challenging objectives in the endeavor for efficient autonomous collision-free navigation. This paper proposes an intelligent technique for speed control of a wheeled mobile robot using a combination of fuzzy logic and supervised machine learning (SML). The technique is appropriate for flexible leader-follower formation control on straight paths where a follower robot maintains a safely varying distance from a leader robot. A fuzzy controller specifies the ultimate distance of the follower to the leader using the measurements obtained from two ultrasonic sensors. An SML algorithm estimates a proper speed for the follower based on the ultimate distance. Simulations demonstrated that the proposed technique appropriately adjusts the follower robot's speed to maintain a flexible formation with the leader robot.Web of Science2110art. no. 343

    A layered fuzzy logic controller for nonholonomic car-like robot

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    A system for real time navigation of a nonholonomic car-like robot in a dynamic environment consists of two layers is described: a Sugeno-type fuzzy motion planner; and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including right and left views to identify the next step to the goal. A Sugeno-type fuzzy motion planner of four inputs one output is introduced to give a clear direction to the robot controller. The second stage is a modified proportional navigation based fuzzy controller based on the proportional navigation guidance law and able to optimize the robot's behavior in real time, i.e. to avoid stationary and moving obstacles in its local environment obeying kinematics constraints. The system has an intelligent combination of two behaviors to cope with obstacle avoidance as well as approaching a target using a proportional navigation path. The system was simulated and tested on different environments with various obstacle distributions. The simulation reveals that the system gives good results for various simple environments

    A Two-Wheeled Vehicle Navigation System Based on a Fuzzy Logic Controller

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    The paper deals with a two-wheeled vehicle,namely ESG-2 (Extended Segway-like Generation- 2) navigation control system using a fuzzy logic controller. The vehicle employs two wheels left and right independently which are controlled independently using a fuzzy logic controller respectively. The controllers deal with a compact and implementable application for the normal using with a person (human with 60kg weight in average) loaded on the vehicle. A modified infrared-based range sensor system is applied to the vehicle as a tilt sensor and it is incorporated with an accelerometer to control its response in case of the dynamics disturbances. The fuzzy controller runs in tilt-mode while a reference tilt using a potentiometer (as steer system) is taken into account for navigating the vehicle. From the simulation using MATLAB @ and experiments it is obvious that the prototype of ESG-2 is quite challenging to be developed in the future

    Sliding Mode Control for Trajectory Tracking of a Non-holonomic Mobile Robot using Adaptive Neural Networks

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    In this work a sliding mode control method for a non-holonomic mobile robot using an adaptive neural network is proposed. Due to this property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a nominal kinematic model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate for the dynamics of the robot. A neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold, using an online adaptation scheme. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown non-linear dynamics. Also, the proposed control technique can reduce the steady-state error using the online adaptive neural network with sliding mode control; the design is based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling mobile robots with large dynamic uncertaintiesFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin
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