Simulator of the navigation equipped with LIDAR of the mobile robot based on the neural network

In this paper a nonholonomic mobile robot with completely unknown dynamics is discussed. A mathematical model has been considered and an efficient neural network is developed, which ensures guaranteed tracking performance leading to stability of the system. The neural network assumes a single layer structure, by taking advantage of the robot regressor dynamics that expresses the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot dynamic parameters. No assumptions relating to the boundedness is placed on the unmodeled disturbances. It is capable of generating real-time smooth and continuous velocity control signals that drive the mobile robot to follow the desired trajectories. The proposed approach resolves speed jump problem existing in some previous tracking controllers. Further, this neural network does not require offline training procedures. Lyapunov theory has been used to prove system stability. The practicality and effectiveness of the proposed tracking controller are demonstrated by simulation and comparison results.

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Application of Biological Learning Theories to Mobile Robot Avoidance and Approach Behaviors

Advances in Complex Systems ◽

10.1142/s0219525998000065 ◽

1998 ◽

Vol 01 (01) ◽

pp. 79-114 ◽

Cited By ~ 25

Author(s):

Carolina Chang ◽

Paolo Gaudiano

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Learning Theories ◽

Light Sources ◽

Approach And Avoidance ◽

Control Approach ◽

Avoidance Behaviors ◽

The Neural Network ◽

Approach Behaviors ◽

Biological Learning

We present a neural network that learns to control approach and avoidance behaviors in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around an environment cluttered with obstacles and light sources. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot's sensors. In this article we provide a detailed presentation of the model, and show our results with the Khepera and Pioneer 1 mobile robots.

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Operation of the mobile robot using flick motion Switching of the control method using the neural network

Transactions of the JSME (in Japanese) ◽

10.1299/transjsme.15-00035 ◽

2015 ◽

Vol 81 (829) ◽

pp. 15-00035-15-00035

Author(s):

Tomonori YAMAMOTO ◽

Satoru SHIBATA

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Control Method ◽

The Neural Network

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Neural network control of a wheeled mobile robot based on optimal trajectories

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420916077 ◽

2020 ◽

Vol 17 (2) ◽

pp. 172988142091607

Author(s):

Pavol Bozek ◽

Yury L Karavaev ◽

Andrey A Ardentov ◽

Kirill S Yefremov

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Intelligent System ◽

Equations Of Motion ◽

Network Control ◽

Neural Network Control ◽

The Neural Network ◽

Euler’S Elastica ◽

Wheeled Robot ◽

Artificial Neural

This article is concerned with developing an intelligent system for the control of a wheeled robot. An algorithm for training an artificial neural network for path planning is proposed. The trajectory ensures steering optimal motion from the current position of the mobile robot to a prescribed position taking its orientation into account. The proposed control system consists of two artificial neural networks. One of them serves to specify the position and the size of the obstacle, and the other forms a continuous trajectory to reach it, taking into account the information received, the coordinates, and the orientation at the point of destination. The neural network is trained on the basis of samples obtained by modeling the equations of motion of the wheeled robot which ensure its motion along trajectories in the form of Euler’s elastica.

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A position estimation system for mobile robots using a monocular image of a 3-D landmark

Robotica ◽

10.1017/s0263574700017987 ◽

1994 ◽

Vol 12 (5) ◽

pp. 431-441 ◽

Cited By ~ 2

Author(s):

Kyoung C. Koh ◽

Jae S. Kim ◽

Hyung S. Cho

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Estimation Algorithm ◽

Position Estimation ◽

Camera System ◽

System A ◽

The Neural Network ◽

Monocular Image ◽

Estimation System ◽

Learning Data

SUMMARYThis paper presents an absolute position estimation system for a mobile robot moving on a flat surface. In this system, a 3-D landmark with four coplanar points and a non-coplanar point is utilized to improve the accuracy of position estimation and to guide the robot during navigation. Applying theoretical analysis, we investigate the image sensitivity of the proposed 3-D landmark compared with the conventional 2-D landmark. In the camera calibration stage of the experiments, we employ a neural network as a computational tool. The neural network is trained from a set of learning data collected at various points around the mark so that the extrinsic and intrinsic parameters of the camera system can be resolved. The overall estimation algorithm from the mark identification to the position determination is implemented in a 32-bit personal computer with an image digitizer and an arithmetic accelerator. To demonstrate the effectiveness of the proposed 3-D landmark and the neural network-based calibration scheme, a series of navigation experiments were performed on a wheeled mobile robot (LCAR) in an indoor environment. The results show the feasibility of the position estimation system applicable to mobile robot's real-time navigation.

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The neural network model RuleNet and its application to mobile robot navigation

Fuzzy Sets and Systems ◽

10.1016/0165-0114(95)00351-7 ◽

1997 ◽

Vol 85 (2) ◽

pp. 287-303 ◽

Cited By ~ 34

Author(s):

Nadine Tschichold-Gürman

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Network Model ◽

Neural Network Model ◽

Robot Navigation ◽

Mobile Robot Navigation ◽

The Neural Network

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Neuro-Evolution of Mobile Robot Controller

MENDEL ◽

10.13164/mendel.2019.1.039 ◽

2019 ◽

Vol 25 (1) ◽

pp. 39-42

Author(s):

Ivan Sekaj ◽

Ladislav Cíferský ◽

Milan Hvozdík

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Mobile Robot ◽

Simulation Environment ◽

Robot Controller ◽

2D Simulation ◽

Neural Network Controller ◽

The Neural Network ◽

Network Controller ◽

Design For Control

We present a neuro-evolution design for control of a mobile robot in 2D simulation environment. The mobile robot is moving in unknown environment with obstacles from the start position to the goal position. The trajectory of the robot is controlled by a neural network – based controller which inputs are information from several laser beam sensors. The learning of the neural network controller is based on an evolutionary approach, which is provided by genetic algorithm.

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Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

Mathematics ◽

10.3390/math8050855 ◽

2020 ◽

Vol 8 (5) ◽

pp. 855

Author(s):

Daniel Teso-Fz-Betoño ◽

Ekaitz Zulueta ◽

Ander Sánchez-Chica ◽

Unai Fernandez-Gamiz ◽

Aitor Saenz-Aguirre

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Navigation System ◽

Reaction Times ◽

Semantic Segmentation ◽

Indoor Navigation ◽

Autonomous Mobile Robot ◽

The Neural Network ◽

Indoor Navigation System ◽

Real Scenario

In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.

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