Artificial neural network based local motion planning of a wheeled mobile robot

Engedy, Istvan; Horväth, Gábor

doi:10.1109/cinti.2010.5672245

Cited by 33 publications

(25 citation statements)

References 8 publications

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“…This section describes the computer simulation result comparisons between the previously developed techniques [13,14] and proposed CN-Fuzzy architecture in the same environment.…”

Section: Comparison With Previous Developed Techniquesmentioning

confidence: 99%

“…In this section, the simulation result comparison has been made between the previous technique [14] and proposed CN-Fuzzy architecture in the same environment with the obstacles. In [14], the authors have discussed the motion and path planning of a car-like wheeled mobile robot between the stationary obstacles using backpropagation artificial neural network. Figure 12 shows the mobile robot navigation in an environment with obstacles using artificial neural network [14].…”

Section: Second Comparison With Developed Techniquementioning

confidence: 99%

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Cascade Neuro-Fuzzy Architecture Based Mobile- Robot Navigation and Obstacle Avoidance in Static and Dynamic Environments

Pandey

Burse

2016

IJARA

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Real-time navigation in the partially unknown environment is an interesting task for mobile robotics. This article presents the cascade neuro-fuzzy (CN-Fuzzy) architecture for intelligent mobile robot navigation and obstacle avoidance in static and dynamic environments. The array of ultrasonic range finder sensors and sharp infrared range sensors are used to read the front, left and right obstacle distances. The cascade neural network is used to train the robot to reach the goal. Its inputs are the different obstacle distance received from the sensors. The output of the neural network is a turning angle between the robot and goal. The fuzzy architecture is integrated with the cascade neural network to control the velocities of the robot. Successful simulation and experimental results verify the effectiveness of the proposed architecture in both static and dynamic environments. Moreover, the proposed CN-Fuzzy architecture gives better results (in terms of path length) as compared to previously developed techniques, which verifies the effectiveness of the proposed architecture.

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“…This section describes the computer simulation result comparisons between the previously developed techniques [13,14] and proposed CN-Fuzzy architecture in the same environment.…”

Section: Comparison With Previous Developed Techniquesmentioning

confidence: 99%

Section: Second Comparison With Developed Techniquementioning

confidence: 99%

Cascade Neuro-Fuzzy Architecture Based Mobile- Robot Navigation and Obstacle Avoidance in Static and Dynamic Environments

Pandey

Burse

2016

IJARA

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“…[27]. In this work they have presented a neural controller with back propagation technique, which uses potential field for obstacle avoidance and the neural controller is aware of its distance sensor readings and its relative position from the target.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System

Mohantyand¹,

Parhi²

2014

Appl. Math. Inf. Sci.

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Abstract:Nowadays intelligent tools such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. This paper presents a new hybrid technique based on the combination of fuzzy inference system and artificial neural network for addressing navigational problem of autonomous mobile robot. First we developed an adaptive fuzzy controller with four input parameters, two output parameters and three parameters each. Afterwards each adaptive fuzzy controller acts as a single takagi-sugeno type fuzzy inference system, where inputs are front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) (from robot), heading angle (HA) (angle to target) and output corresponds to the wheel velocities ( Left wheel and right wheel) for the mobile robot. The effectiveness, feasibility and robustness of the proposed navigational controller have been demonstrated by means of simulation experiments. The real time experimental results were verified with simulation experiments, showing that the proposed navigational algorithm consistently performs better results to navigate the mobile robot safely in a completely or partially unknown environment.

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“…Many artificial intelligence and nature-inspired methods such as Hybrid Fuzzy Controller [1], Neural network Technique [2], Neuro-fuzzy Controller [3], Adaptive Neuro-fuzzy (ANFIS) [4], Simulated annealing algorithm (SA) [5], Particle swarm optimization (PSO) algorithm [6], Genetic algorithm (GA) [7], and Ant colony optimization algorithm (ACO) [8] have been designed and implemented by researchers for wheeled robot motion planning and static/dynamic collision avoidance. Most of the researchers [2,4,[6][7][8] have focused on static or non-moving obstacle avoidance based motion planning. However, few of them [1,3,5] have considered dynamic or moving obstacles for navigation and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Motion Planning for Autonomous Wheeled Robot using Minimum Fuzzy Rule based Controller with Avoidance of Moving Obstacles

Tiwari,

Guha,

Pandey*

2019

IJITEE

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In this article, the Minimum Fuzzy Rule-Based (MFRB) sensor-actuator controller has designed for dynamic motion planning of a differential drive wheeled robot among the moving, non-moving obstacles and goal in two-dimensional environments. The ring of ultrasonic sensors and infrared sensors have been attached on the front side, left side, and right side of the wheeled robot, which detects the moving obstacles, as well as non-moving obstacles in any environment. This proposed MFRB sensor-actuator controller helps the wheeled robot to move safely in a different scenario. The onboard sensor interpretation data are fed as input to the MFRB controller, and the MFRB controller provides the Pulse Width Modulation (PWM) based wheel velocity commands to both the left and right motors of a wheeled robot. In the numerical simulation and experiment, we have taken one condition that the speed of wheels of the differential drive wheeled robot is at least more than or equal to the rate of the moving obstacles and the moving goal. The numerical simulations are performed through a MATLAB graphical user interface (GUI), and we have used the differential drive wheeled robot to conduct experiments. The presented numerical simulation and experimental results illustrate that the MFRB controller operated wheeled robot has successfully avoided the stationary and nonstationary obstacles in various scenarios.

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Artificial neural network based local motion planning of a wheeled mobile robot

Cited by 33 publications

References 8 publications

Cascade Neuro-Fuzzy Architecture Based Mobile- Robot Navigation and Obstacle Avoidance in Static and Dynamic Environments

Cascade Neuro-Fuzzy Architecture Based Mobile- Robot Navigation and Obstacle Avoidance in Static and Dynamic Environments

A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System

Dynamic Motion Planning for Autonomous Wheeled Robot using Minimum Fuzzy Rule based Controller with Avoidance of Moving Obstacles

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