A New Path Planning Method of Robot Based on Improved Adaptive Genetic Algorithms

2013 ◽  
Vol 278-280 ◽  
pp. 590-593
Author(s):  
Huai Qiang Li ◽  
Ming Xia Shi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Path Planning ◽  
Simulation Experiment ◽  
Planning Method ◽  
Adaptive Genetic Algorithm ◽  
Robot Path Planning ◽  
Adaptive Genetic Algorithms ◽  
Planning Space ◽  
Robot Path

This paper presents a new method of robot path planning which is based on improved adaptive genetic algorithm. On the foundation of building the model in planning space by link-graph, we first gets the feasible paths by using Ford algorithms ,and then adjusts the points of every path by using improved adaptive genetic algorithms to get the best or better path. The simulation experiment shows the advancement of the method.

Research and Implementation of Multi-robot Path Planning Based on Genetic Algorithm

2021 ◽  
Author(s):  
Mengqing Fan ◽  
Jiawang He ◽  
Susheng Ding ◽  
Yuanhao Ding ◽  
Meng Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Robot Path Planning ◽  
Robot Path ◽  
Multi Robot

Path Planning in a Mobile Robot

2019 ◽  
pp. 582-608
Author(s):  
Diego Alexander Tibaduiza Burgos ◽  
Maribel Anaya Vejar
Keyword(s):  
Genetic Algorithms ◽  
Path Planning ◽  
Mobile Robot ◽  
Fitness Function ◽  
Experimental Setup ◽  
Robot Path Planning ◽  
Natural Evolution ◽  
On Line ◽  
Planning Problems ◽  
Robot Path

This chapter presents the development and implementation of three approaches that contribute to solving the mobile robot path planning problems in dynamic and static environments. The algorithms include some items regarding the implementation of on-line and off-line situations in an environment with static and mobile obstacles. A first technique involves the use of genetic algorithms where a fitness function and the emulation of the natural evolution are used to find a free-collision path. The second and third techniques consider the use of potential fields for path planning using two different ways. Brief descriptions of the techniques and experimental setup used to test the algorithms are also included. Finally, the results applying the algorithms using different obstacle configurations are presented and discussed.

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