Autonomous navigation of mobile robot using shallow and deep neural network

2020 ◽  
Vol 7 (2) ◽  
pp. 64
Author(s):  
Nathi Ram Chauhan ◽  
Urfi Khan ◽  
Rituvika Narula
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Deep Neural Network

scholarly journals Path Planning for Autonomous Driving of Mobile Robots using Deep Neural Network based Model Predictive Control

2021 ◽  
Vol 11 (11) ◽  
pp. 31-38
Author(s):  
Kiwon Yeom ◽  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Deep Neural Network ◽  
Optimal Solution ◽  
Autonomous Driving ◽  
Physical Constraints ◽  
Predictive Controller

A car-like mobile robot is a nonlinear affine system, and the mobile robot has physical constraints such as velocity and acceleration. Thus, no satisfactory solution may not be provided during self-driving under unknown environments. Although Model Predictive Control (MPC) has provided good performance in terms of control strategy, it is difficult to optimize the control parameters due to the uncertainty and non-linearity of a process. In this paper, the Deep Neural Networks (DNN) based Model Predictive Controller (MPC) is derived for tracking the given path during self-driving. The proposed DNN MPC produces the global optimal solution which has better performance than traditional MPC in terms of the errors of position and orientation. This paper verifies that the proposed DNN MPC based controller can track the desired path with high precision for the car-like mobile robot. Keywords—Path planning, autonomous driving, mobile robot, deep neural network, model predictive control.

scholarly journals Mobile robot controller using novel hybrid system

2020 ◽  
Vol 10 (1) ◽  
pp. 1027
Author(s):  
Ayman A Abu Baker ◽  
Yazeed Ghadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Processing Time ◽  
Fuzzy Controller ◽  
Robot Controller ◽  
Neuro Fuzzy ◽  
Unstructured Environment ◽  
Adaptive Inference

Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduce that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.

scholarly journals Deep Neural Network-Based System for Autonomous Navigation in Paddy Field

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 71272-71278
Author(s):  
Shyam P. Adhikari ◽  
Gookhwan Kim ◽  
Hyongsuk Kim
Keyword(s):  
Neural Network ◽  
Paddy Field ◽  
Autonomous Navigation ◽  
Deep Neural Network

scholarly journals A New Landmark Detection Approach for Slam Algorithm Applied in Mobile Robot

2020 ◽  
Vol 30 (7) ◽  
pp. 31-36
Author(s):  
Xuan-Ha Nguyen ◽  
Van-Huy Nguyen ◽  
Thanh-Tung Ngo
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Deep Neural Network ◽  
Research Effort ◽  
Measurement Data ◽  
Neural Network Models ◽  
Landmark Detection ◽  
Camera System ◽  
Detection Approach ◽  
Slam Algorithm

Simultaneous Localization and Mapping is a key technique for mobile robot applications and has received much research effort over the last three decades. A precondition for a robust and life-long landmark-based SLAM algorithm is the stable and reliable landmark detector. However, traditional methods are based on laserbased data which are believed very unstable, especially in dynamic-changing environments. In this work, we introduce a new landmark detection approach using vision-based data. Based on this approach, we exploit a deep neural network for processing images from a stereo camera system installed on mobile robots. Two deep neural network models named YOLOv3 and PSMNet were re-trained and used to perform the landmark detection and landmark localization, respectively. The landmark’s information is associated with the landmark data through tracking and filtering algorithm. The obtained results show that our method can detect and localize landmarks with high stability and accuracy, which are validated by laser-based measurement data. This approach has opened a new research direction toward a robust and life-long SLAM algorithm.

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