State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

2013 ◽  
Vol 14 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Xin Ma ◽  
Ya Xu ◽  
Guo-qiang Sun ◽  
Li-xia Deng ◽  
Yi-bin Li
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Mobile Robots

scholarly journals An Inverse Pheromone Approach in a Chaotic Mobile Robot’s Path Planning Based on a Modified Logistic Map

Technologies ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 84
Author(s):  
Eleftherios K. Petavratzis ◽  
Christos K. Volos ◽  
Lazaros Moysis ◽  
Ioannis N. Stouboulos ◽  
Hector E. Nistazakis ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Efficient Method ◽  
Chaotic Motion ◽  
Logistic Map ◽  
Autonomous Mobile Robots ◽  
Motion Pattern ◽  
Major Topic ◽  
Random Bit Generator ◽  
Modified Logistic Map

One major topic in the research of path planning of autonomous mobile robots is the fast and efficient coverage of a given terrain. For this purpose, an efficient method for covering a given workspace is proposed, based on chaotic path planning. The method is based on a chaotic pseudo random bit generator that is generated using a modified logistic map, which is used to generate a chaotic motion pattern. This is then combined with an inverse pheromone approach in order to reduce the number of revisits in each cell. The simulated robot under study has the capability to move in four or eight directions. From extensive simulations performed in Matlab, it is derived that motion in eight directions gives superior results. Especially, with the inclusion of pheromone, the coverage percentage can significantly be increased, leading to better performance.

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