Study on Improved APF Algorithm for Autonomous Mobile Robot

2014 ◽  
Vol 519-520 ◽  
pp. 1337-1341 ◽  
Author(s):  
Xiao Meng Shu ◽  
Da Ming Jiang ◽  
Lian Dai
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Local Minimum ◽  
Minimum Point ◽  
Autonomous Mobile Robot ◽  
Simulation Environment ◽  
Local Minimum Point

In algorithms of obstacle avoidance for autonomous mobile robot, APF algorithm is simple, real-time and smooth, but has some limitations for solving problems. For example, the local minimum point may trap mobile robots before reaching its goal. Even though many improved APF algorithms have been put forward, few articles describe the process in detail to show how these algorithms are applied. Considering above factors, this paper focuses on embodiment of abstract improved theory for APF algorithm by showing some changes with formulas and parameters. The whole work has been done in simulation environment. According to the results this paper draws a conclusion.

Research on Navigation and Obstacle Avoidance Algorithm for Autonomous Mobile Robot in Narrow Area

2011 ◽  
Vol 464 ◽  
pp. 204-207
Author(s):  
Huan Xun Li ◽  
Jun Jie Shen ◽  
Shuai Guo
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Extraction Method ◽  
Field Method ◽  
Autonomous Mobile Robot ◽  
Environment Modeling ◽  
Feature Extraction Method ◽  
Potential Field Method ◽  
Narrow Area

In order to improve the accuracy and security when autonomous mobile robot moves in narrow area, a real-time navigation and obstacle avoidance algorithm is put forward. The feature extraction method is used to search for the path points, and the angle potential field method is used to search for the target angle. Based on the two methods more accurate environment modeling and navigation for mobile robot in narrow area is realized. The algorithm has been used successfully in the household robot, and the experiment results show it’s accurate and real-time.

Development of Autonomous Mobile Robot for Obstacle Avoidance

1993 ◽  
Vol 5 (5) ◽  
pp. 481-486 ◽  
Author(s):  
Masafumi Uchida ◽  
◽  
Syuichi Yokoyama ◽  
Hideto Ide ◽  
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Potential Method ◽  
Working Environment ◽  
Planning Problem ◽  
Autonomous Mobile Robot ◽  
The Real

The potential method is superior for solving the problem of motion planning; however, it must address the problem of the real-time generation of potential field. Obstacle avoidance is a motion planning problem. In a previous study, we investigated the real-time generation of potential field. Based on parallel processing with element group, we proposed the system by Sensory Point Moving (SPM) method. As a result of computer simulation, it was confirmed that the SPM method is effective for generating an obstacle avoidance path in 2-D and a more complex working environment like a 3-D one. In this paper, we discuss the development of autonomous mobile robot for obstacle avoidance based on the SPM method.

1P1-K13 Real-time Obstacle Avoidance for Car-Type Autonomous Mobile Robot(Wheeled Robot/Tracked Vehicle)

2011 ◽  
Vol 2011 (0) ◽  
pp. _1P1-K13_1-_1P1-K13_4 ◽  
Author(s):  
Masanori Sato ◽  
Masataka Hirai ◽  
Tetsuo Tomizawa ◽  
Shunsuke Kudoh ◽  
Takashi Suehiro
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Autonomous Mobile Robot ◽  
Tracked Vehicle ◽  
Wheeled Robot

A Real-time Algorithm for Obstacle Avoidance of Autonomous Mobile Robots

Robotica ◽  
1992 ◽  
Vol 10 (3) ◽  
pp. 217-227 ◽  
Author(s):  
Huang Han-Pang ◽  
Lee Pei-Chien
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Time Algorithm ◽  
Autonomous Mobile Robots ◽  
Computational Time ◽  
Avoidance Problem

SUMMARYA real-time obstacle avoidance algorithm is proposed for autonomous mobile robots. The algorithm is sensor-based and consists of a H-mode and T-mode. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. It will be shown that the algorithm is more efficient and more robust than other sensor-based algorithms. In addition, the algorithm will guarantee a solution for the obstacle avoidance problem. Since the algorithm only takes up a small computational time, it can be implemented in real time.

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chittaranjan Paital ◽  
Saroj Kumar ◽  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Prasant Ranjan Dhal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Simulation Software ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

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