Neural network-based approaches for mobile robot navigation in static and moving obstacles environments

2018 ◽  
Vol 12 (1) ◽  
pp. 55-67 ◽  
Author(s):  
Ngangbam Herojit Singh ◽  
Khelchandra Thongam
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Moving Obstacles

Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial Internet of things environment

2020 ◽  
Vol 1 (1) ◽  
pp. 59-67
Author(s):  
P. Pei ◽  
Yu. N. Petrenko
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Industrial Internet Of Things ◽  
Navigation Problem ◽  
Automatic Navigation ◽  
Industrial Internet ◽  
Fuzzy Neural ◽  
Navigation Control

Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation.The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.

Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture

2019 ◽  
Vol 16 (2) ◽  
pp. 275-286 ◽  
Author(s):  
Anish Pandey ◽  
Abhishek Kumar Kashyap ◽  
Dayal R. Parhi ◽  
B.K. Patle
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Experimental Studies ◽  
Sensor Data ◽  
Mobile Robot Navigation ◽  
Control Technique ◽  
Inference System ◽  
Content Type ◽  
Moving Obstacles ◽  
Moving Obstacle

PurposeThis paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles.Design/methodology/approachThe three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal.FindingsGraphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture.Originality/valueThis paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.

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