Obstacle avoidance in mobile robot using Neural Network

Chi, Kai-Hui; Lee, Min‐Fan Ricky

doi:10.1109/cecnet.2011.5768815

Cited by 48 publications

(28 citation statements)

References 7 publications

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“…An ANN control system is developed to guide the mobile robots traverse through a maze with arbitrary obstacles. Sonar and laser range finder are inputs for robot to sense the pattern of environment [10]. A visual servo system is developed for navigating an autonomous mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing and analysis using autonomous mobile robot with onboard micro-spectrometer

Lee

Chiu

Zhuo

2013

2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY)

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There has been an increasing interest in remote sensing and analysis of the environment. The applications include disaster monitoring, exploration, and natural resource protection. However, the human operation is labor intensive, time consuming and hazardous. The sensors fixed on-site in the environment suffer from limited sensing coverage and still requires human's on-site installation and maintenance. This paper proposed a novel system for the remote sensing and analysis of the environment using a mobile robot with on-board micro-spectrometer. A tracked locomotion is designed to adapt to tough terrains. The FLC (fuzzy logic control) navigate the mobile robot traversing trough the environment and collects the samples. The onboard micro-spectrometer system senses the samples and pattern of the solution spectrum is recognized through the ANN (artificial neural network). The results show the system can effectively and efficiently sample and classify several nominal solutions. The robot successfully transmits the spectrum and analysis to the remote control station through the proposed distributed architecture. The proof-of-concept demonstrates an unmanned operated chemical lab in motion for the remote sensing and analysis of the environment by integrating the micro-spectrometer with the autonomous mobile robot.

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Section: Introductionmentioning

confidence: 99%

Remote sensing and analysis using autonomous mobile robot with onboard micro-spectrometer

Lee

Chiu

Zhuo

2013

2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY)

Self Cite

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“…A Webots Pro Simulation software was used to model and program the e-puck robot and fuzzy algorithm and to test an environment. Chi and Less [12], showed an artificial neural networks was applied to learn the environment from sensory data to move along a collision free trajectory. In [13], the navigation of non-holonomic robot was discussed using neuro-fuzzy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation

2014

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This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles.

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“…These include lasers and infrared [7,8], vision systems [9,10], and wall following using ultrasonic sensors [11–13]. Extension theory was proposed in 1983 to solve contradictions and incompatibility problems.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Obstacle-Avoidance Control for an Autonomous Omni-Directional Mobile Robot Based on Extension Theory

Pai

Hsieh

Lai

2012

Sensors

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Abstract:The paper demonstrates a following robot with omni-directional wheels, which is able to take action to avoid obstacles. The robot design is based on both fuzzy and extension theory. Fuzzy theory was applied to tune the PMW signal of the motor revolution, and correct path deviation issues encountered when the robot is moving. Extension theory was used to build a robot obstacle-avoidance model. Various mobile models were developed to handle different types of obstacles. The ultrasonic distance sensors mounted on the robot were used to estimate the distance to obstacles. If an obstacle is encountered, the correlation function is evaluated and the robot avoids the obstacle autonomously using the most appropriate mode. The effectiveness of the proposed approach was verified through several tracking experiments, which demonstrates the feasibility of a fuzzy path tracker as well as the extensible collision avoidance system.

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Obstacle avoidance in mobile robot using Neural Network

Cited by 48 publications

References 7 publications

Remote sensing and analysis using autonomous mobile robot with onboard micro-spectrometer

Remote sensing and analysis using autonomous mobile robot with onboard micro-spectrometer

Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation

Implementation of Obstacle-Avoidance Control for an Autonomous Omni-Directional Mobile Robot Based on Extension Theory

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