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Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements

Tomislav Pavlic, Krunoslav Kušec, Danijel Radočaj, Alen Britvić, Marin Lukas, Vladimir Milić, Mladen Crneković
2021 Applied Sciences  
The learning process is based on a virtual dynamic model of a mobile robot, identical to a real mobile robot.  ...  The mobile robot's motion with developed artificial neural networks and genetic algorithms is defined.  ...  It is possible to visualize an entire neural network in real-time with the dedicated button.  ... 
doi:10.3390/app11062786 fatcat:bqlu6rked5fphluo2dzbparzuu

Vision-Based Mobile Robot Controllers: A Scientific Review

Adnan Mohsin Abdulazeez, Fayez Saeed Faizi
2021 Turkish Journal of Computer and Mathematics Education  
The crucial differences between this kind of robot and the controlled ones are their ability to move on their own and make decisions based on their observations of the world around them.  ...  The world of mobile robot controllers is discussed in this paper, and the latest trends were reviewed.  ...  [61] combined Artificial Neural Networks (ANN) and Finite State Machines (FSM) to build an approach for mobile robot control.  ... 
doi:10.17762/turcomat.v12i6.2695 fatcat:hxdsdt2kl5hs3eswac4ckeuv6i

Stereo-Vision Mobile Robotics Navigation through Metric-Topological Learning

2013 Advances in Robotics & Automation  
The architecture takes advantage of the high throughput of neural networks for the processing of camera images.  ...  Neural networks, fuzzy logic, and reinforcement and evolutionary-learning methods can be used to implement basic behavioral functions". Control system was organized in a top-bottom hierarchy.  ... 
doi:10.4172/2168-9695.1000e123 fatcat:4ykllwtxwbap7l4azojf5weo7m

Visual Perception based Motion Planning of Mobile Robot using Road Sign

Pradipta KDas, S. C. Mandhata, H.S Behera, S.N. Patro
2012 International Journal of Computer Applications  
In this paper a new method of road map based navigation is proposed. A vision based motion planning of a mobile robot is implemented in a predefined road map.  ...  In our realization the robot moves towards a junction and at each junction takes a photograph of the road sign map and an image matching algorithm is performed at the host machine to compare the captured  ...  For polymorphism ergod done according to the chaotic track and not limited to an object function, so it is very robust in avoiding the neural network system to trap in a local minimum.  ... 
doi:10.5120/7422-0374 fatcat:ewfwji3ykjczdf2dgmyohx4nli

A Markerless Deep Learning-based 6 Degrees of Freedom PoseEstimation for with Mobile Robots using RGB Data [article]

Linh Kästner, Daniel Dimitrov, Jens Lambrecht
2020 arXiv   pre-print
In this work we propose a method to deploy state of the art neural networks for real time 3D object localization on augmented reality devices.  ...  The results are implemented into an Augmented Reality application for intuitive robot control and sensor data visualization.  ...  In [7] we developed an AR-based application to control the robot with gestures and visualize its navigation data, like robot sensors, path planing information and environment maps.  ... 
arXiv:2001.05703v1 fatcat:3xluik5x3bf4rin6djteaawbx4

Mobile robots interacting with obstacles control based on artificial intelligence

Duc Chuyen Tran, Van Hoa Roan, Duc Dien Nguyen, Tung Lam Nguyen
2021 International Conference on Research in Intelligent and Computing in Engineering  
The goal of the experimental studies is to navigate the mobile robot to learn the best possible action to move in real-world environments when facing fixed and mobile obstacles.  ...  The research results will be the basis for the design and construction of control algorithms for mobile robots and industrial robots applied in programming techniques and industrial factory automation  ...  The tests are conducted on the Gazebo emulator using a high-profile computer with a mobile robot, with its open-source extension to perform automated navigation tasks for mobile robots, and then carried  ... 
doi:10.15439/2021r21 dblp:conf/rice/TranRN021 fatcat:mfncugjgfbeq5ndxcxdphsq3s4

Convolutional Neural Network Based Path Navigation of a Differential Drive Robot in an Indoor Environment

2019 International journal of recent technology and engineering  
The current work illustrates a vision-guided approach to a real-time robot navigation system and the implementation of Faster Convolutional Neural Networks (FCNN) to train and detect objects with multiple  ...  The processor and mobile robot communicate wirelessly for simultaneous localization and path planning.  ...  [2] in 2017 proposed "Development of Robot Navigation Method Based on Single Camera Vision Using Deep Learning" which utilizes vision for robot navigation on roads for real-time approach.  ... 
doi:10.35940/ijrte.b1099.0782s319 fatcat:chvxgehslreqxgtkkqblugul3q

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ben Beklisi Kwame Ayawli, Ryad Chellali, Albert Yaw Appiah, Frimpong Kyeremeh
2018 Journal of Advanced Transportation  
The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.  ...  Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles.  ...  Chi and Lee [157] proposed neural network control strategy with backpropagation model to control mobile robot to navigate through obstacles without collision.  ... 
doi:10.1155/2018/8269698 fatcat:fpadrzacozbdnkcqkjduokmoc4

Computer Vision Positioning and Local Obstacle Avoidance Optimization Based on Neural Network Algorithm

Lei Yang, Weimin Lei, Arpit Bhardwaj
2022 Computational Intelligence and Neuroscience  
This study uses neural network algorithms to systematically optimize computer vision positioning and also studies the accuracy optimization of local obstacle avoidance, aiming to promote its better development  ...  Due to the rapid development of social computerization and smart devices, there is an increasing demand for indoor positioning of mobile robots in the robotics field, so it is very important to realize  ...  include various environmental information parameters. e sweeping robot in this study uses a camera to capture road images in real time and uses conventional neural networks to process the images to obtain  ... 
doi:10.1155/2022/3061910 pmid:35401716 pmcid:PMC8993561 fatcat:lszqxf27czcy5n3twrbr5lzome

Intelligent Obstacle Avoidance Algorithm for Mobile Robots in Uncertain Environment

Liwei Guan, Yu Lu, Zhijie He, Xi Chen, Shan Zhong
2022 Journal of Robotics  
In a nondeterministic environment, a mobile robot intelligent obstacle avoidance algorithm based on an improved fuzzy neural network with self-learning is firstly proposed.  ...  Therefore, it is an important step to complete the automatic learning of obstacle avoidance for mobile robots.  ...  Fire Control System for Intelligent Security"(no.2020Y0021).  ... 
doi:10.1155/2022/8954060 fatcat:qb6r7cxjvbgjrdloiwattl4x7m

Deep reinforcement learning based mobile robot navigation: A review

Kai Zhu, Tao Zhang
2021 Tsinghua Science and Technology  
There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks.  ...  Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities  ...  Generally, the deep neural network is trained in a simulation environment before being deployed in a real robot for real-time navigation decision making.  ... 
doi:10.26599/tst.2021.9010012 fatcat:7rrkw43mqffdjnmgvxkxjwrhkm

Robot Navigation And Localization Based On The Rat'S Brain Signals

Endri Rama, Genci Capi, Shigenori Kawahara
2017 Zenodo  
The mobile robot ability to navigate autonomously in its environment is very important.  ...  As the first step to incorporate the rat's navigation strategy into the robot control, we analyzed the rats' strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs  ...  ROBOT AND ARTIFICIAL NEURAL NETWORK In this section, the architecture of the neural network used to control the robot and the robot characteristics is defined.  ... 
doi:10.5281/zenodo.1129675 fatcat:g5ds7brsjzdk3ekcvdnsbf4hly

Robot visual navigation estimation and target localization based on neural network

Yanping Zhao, Rajeev Kumar Gupta, Edeh Michael Onyema
2022 Paladyn: Journal of Behavioral Robotics  
Finally, it elaborates on the breadth-first search based on regression neural network (RNN) method, the Voronoi skeleton diagram method, the algorithm principle, and how to navigate by the planning path  ...  To overcome these problems, a mobile robot path planning navigation system based on panoramic vision was proposed. This method first describes the structure and functions of the navigation system.  ...  For the navigation tasks of mobile robots, algorithms need to meet high requirements in terms of real-time, effectiveness, and path safety.  ... 
doi:10.1515/pjbr-2022-0005 fatcat:wn6iu5xt3femjhwxuorim4rxga

Enhancing Neural Based Obstacle Avoidance with CPG Controlled Hexapod Walking Robot

Petr Cízek, Jan Faigl, Jan Bayer
2017 Conference on Theory and Practice of Information Technologies  
The proposed extension uses Recurrent Neural Network (RNN) to map the output of the LGMD on the input of the CPG to enhance collision avoiding behavior of the robot in cluttered environments.  ...  to the locomotion control unit based on the Central Pattern Generator (CPG) of a hexapod walking robot.  ...  SGS16/235/OHK3/3T/13 to Petr Čížek is also gratefully acknowledged.  ... 
dblp:conf/itat/CizekFB17 fatcat:3ks4egwnaval5jeb3rslq64vee

End-to-end Learning for Autonomous Crop Row-following

Marianne Bakken, Richard J.D. Moore, Pål From
2019 IFAC-PapersOnLine  
Our approach employs a deep convolutional neural network (DCNN) and an end-to-end learning strategy.  ...  Our approach employs a deep convolutional neural network (DCNN) and an end-to-end learning strategy.  ...  Preliminary field trials To test the robustness of our fine-tuned network to real world conditions, we implemented our DCNN on board a mobile robot (section 3.5) and drove it through the tunnels found  ... 
doi:10.1016/j.ifacol.2019.12.505 fatcat:iiv3nbomijbthektxht7mvqgui
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