MULTI-ROBOT DYNAMIC FORMATION PATH PLANNING WITH IMPROVED POLYCLONAL ARTIFICIAL IMMUNE ALGORITHM

2014 ◽  
Vol 42 (4) ◽  
Author(s):  
Lixia Deng ◽  
Xin Ma ◽  
Jason Gu ◽  
Yibin Li
Keyword(s):  
Path Planning ◽  
Immune Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Dynamic Formation ◽  
Multi Robot

Dual Aircrafts Path Planning for Forest-Fire Rescue by Artificial Immune Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1659-1663 ◽  
Author(s):  
Li Feng Liu ◽  
Shu Qing Zhang
Keyword(s):  
Path Planning ◽  
Forest Fire ◽  
Immune Algorithm ◽  
Experimental Results ◽  
Smoothing Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Gradient Smoothing ◽  
In Fire

Path planning for dual aircrafts is preceded by artificial immune algorithm in forest fire rescue. We analysis and compare several parameters which will influence path planning. Based on above results we sum the reasonable parameters which should be used in further experiment. Moreover, to improve the operability of aircraft, restrictions of gradient smoothing algorithm, curvature smoothing algorithm are performed to smooth track in vertical aspect. Experimental results show that we can successfully get the satisfied flight paths for dual aircrafts in fire rescue under complex circumvent. At the same time the paths we planned have certain practical value by consideration the aircrafts fly-ability constraints.

Mobile Robot Path Planning and Research in the Improved Artificial Immune Algorithm

2012 ◽  
Vol 466-467 ◽  
pp. 864-869 ◽  
Author(s):  
Yuan Bin Hou ◽  
Wei Wang ◽  
Xiao Yue Lu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Immune Algorithm ◽  
Initial Population ◽  
Robot Path Planning ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Selection Operator ◽  
Robot Path

Aim at local optimal problem in the path planning of mobile robot by artificial immune algorithm, it is proposed that the improved artificial immune algorithm of mobile robot path planning. Based on artificial immunity algorithm, the potential function method of an artificial potential field is used in this algorithm, improving randomness of the initial population of the artificial immune algorithm, then the algorithm make initial population turn to evolutionary operation through crossover, variance and selection operator to get optimum antibody. The simulation results showed that this algorithm is easy to get the optimal path, at the same time, increasing the speed of the path planning, and the length of the optimal path planning is less 28.5% compare with the traditional immune algorithm.

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