Local Obstacle Avoidance Using Obstacle-Dependent Gaussian Potential Field for Robot Soccer

2016 ◽  
pp. 539-549 ◽  
Author(s):  
Dong-Ok Kim ◽  
Da-Yeon Lee ◽  
Jae-Il Oh ◽  
Tae-Hoon Kang ◽  
Tae-Koo Kang
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Robot Soccer ◽  
Gaussian Potential

scholarly journals A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jang-Ho Cho ◽  
Dong-Sung Pae ◽  
Myo-Taeg Lim ◽  
Tae-Koo Kang
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Calculation Method ◽  
Potential Field ◽  
Autonomous Vehicles ◽  
Field Calculation ◽  
Gaussian Potential

A new obstacle avoidance method for autonomous vehicles calledobstacle-dependent Gaussian potential field(ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF performed the best in most cases.

scholarly journals Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field

2021 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Xun Yan ◽  
Dapeng Jiang ◽  
Runlong Miao ◽  
Yulong Li
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Shape Change ◽  
Artificial Potential Field ◽  
Generation Algorithm ◽  
Time Performance ◽  
Movement Ability ◽  
Window Approach ◽  
Avoidance Problem

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

scholarly journals Autonomous Generation of the Potential Field on Obstacle Avoidance Problem.

1992 ◽  
Vol 10 (4) ◽  
pp. 510-519
Author(s):  
Shuichi YOKOYAMA ◽  
Masabumi UCHIDA ◽  
Kei FUKUSHIMA ◽  
Toshio AKINUMA
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Avoidance Problem

Research of Robot of Obstacle Avoidance and Navigation

2012 ◽  
Vol 160 ◽  
pp. 180-184
Author(s):  
Xiao Chen Lai ◽  
Si Min Lu ◽  
Xi Chen ◽  
Pei Feng Qiu ◽  
Li Kun Li
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Inertial Navigation ◽  
Artificial Potential Field ◽  
Design And Implementation

Based on algorithms of artificial potential field and inertial navigation positioning, the design and implementation of robot is introduced with the features of obstacle avoidance and navigation automatically. Experiments show that the robot can navigate to destination effectively without any collision.

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