Velocity planning for a mobile robot to track a moving target — a potential field approach

2009 ◽  
Vol 57 (1) ◽  
pp. 55-63 ◽  
Author(s):  
L. Huang
Keyword(s):  
Mobile Robot ◽  
Potential Field ◽  
Moving Target ◽  
Field Approach ◽  
Velocity Planning

Mobile Robot Dynamic Path Planning Based on Artificial Potential Field Approach

2012 ◽  
Vol 490-495 ◽  
pp. 994-998 ◽  
Author(s):  
Pu Shi ◽  
Jian Ning Hua
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Function ◽  
Relative Position ◽  
Potential Field ◽  
Field Method ◽  
Attractive Potential ◽  
Artificial Potential Field ◽  
Field Approach ◽  
Dynamic Path Planning

Artificial potential field based mobile robot path planning approaches have been widely used. However, most methods are applied in the static environment where the target and the obstacles are stationary. In this paper, a potential field approach used in dynamic situation is proposed. Its major characteristics include a new attractive potential function as well as a repulsive potential function. The former takes the relative position and velocity between the robot and the target into consideration; the latter takes into account the relative position and velocity between the robot and the obstacles. The proposed approach guarantees the robot can track the moving target while escape from moving obstacles. Simulation experiments are carried out and the results demonstrate the effectiveness of the new potential field method.

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.

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