scholarly journals Time-efficient A* Algorithm for Robot Path Planning

2016 ◽  
Vol 23 ◽  
pp. 144-149 ◽  
Author(s):  
Akshay Kumar Guruji ◽  
Himansh Agarwal ◽  
D.K. Parsediya
Keyword(s):  
Path Planning ◽  
Robot Path Planning ◽  
A Algorithm ◽  
Robot Path

An improved A* algorithm for the industrial robot path planning with high success rate and short length

2018 ◽  
Vol 106 ◽  
pp. 26-37 ◽  
Author(s):  
Bing Fu ◽  
Lin Chen ◽  
Yuntao Zhou ◽  
Dong Zheng ◽  
Zhiqi Wei ◽  
...  
Keyword(s):  
Path Planning ◽  
Success Rate ◽  
Industrial Robot ◽  
Short Length ◽  
High Success Rate ◽  
Robot Path Planning ◽  
A Algorithm ◽  
Robot Path

Multi-Robot Fish Path Planning Based on the Modified A* Algorithm

2014 ◽  
Vol 568-570 ◽  
pp. 1054-1058 ◽  
Author(s):  
Qiang Hong ◽  
Mei Xiao Chen ◽  
Yan Song Deng
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Experimental Results ◽  
Robot Path Planning ◽  
A Algorithm ◽  
Optimal Path Planning ◽  
2D Simulation ◽  
Robot Fish ◽  
Robot Path ◽  
Multi Robot

Based on improved A* algorithm, this paper proposes the optimal path planning of robot fish in globally known environment, so as to achieve better coordination between the robot fish by means of improving their path planning. In the known obstacle environment which is rasterized, target nodes are generated via smoothing A* algorithm. The unnecessary connection points are removed then and the path is smoothed at the turning points. That improved algorithm, in combination with distributed scroll algorithms, is applied to multi-robot path planning in an effort to optimize the path with the avoidance of collision. The experimental results on the 2D simulation platform have verified the feasibility of that method.

An Adaptive Path Planning Based on Improved Fuzzy Neural Network for Multi-Robot Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 1396-1424
Author(s):  
Zhiguo Shi ◽  
Huan Zhang ◽  
Jingyun Zhou ◽  
Junming Wei
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Fuzzy Neural Network ◽  
Robot Path Planning ◽  
A Algorithm ◽  
Effective Performance ◽  
Robot Systems ◽  
Fuzzy Neural ◽  
Adaptive Control Strategy ◽  
Robot Path

The fuzzy neural network (FNN) is the combination of fuzzy theory with neural network, which has advantages of validity and adaptability in robot path planning. However, the path planning based on the FNN is not optimal because of the limitations of the subjective experience and motion mutation and the dead-zone. In this paper, FNN is improved by using A* graph search algorithm to guarantee an optimal path, providing the rationality and the feasibility, in which the grid map is divided into two stages, including the A* algorithm in the first stage and FNN in the second stage. In addition, a neural network based on adaptive control strategy is introduced to compensate the sensor failure and ensures the stability, which is caused by the loss of data and information uncertainty. The simulation results show that the approach is with effective performance in the robot path planning.

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