A Knowledge Based GA for Path Planning of Multiple Mobile Robots in Dynamic Environments

2006 ◽  
Author(s):  
Simon Yang ◽  
Yanrong Hu ◽  
Max Meng
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Dynamic Environments ◽  
Multiple Mobile Robots ◽  
Knowledge Based

On-Line Centralized Path Planning of Multiple Mobile Robots in Dynamic Environments

2012 ◽  
Vol 155-156 ◽  
pp. 1074-1079
Author(s):  
Zi Hui Zhang ◽  
Yue Shan Xiong
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Dynamic Environments ◽  
Characteristic Line ◽  
Multiple Mobile Robots ◽  
Time Performance ◽  
Planning Algorithm ◽  
On Line ◽  
Path Planning Algorithm

To study the path planning problem of multiple mobile robots in dynamic environments, an on-line centralized path planning algorithm is proposed. It is difficult to obtain real-time performance for path planning of multiple robots in dynamic environment. The harmonic potential field for multiple mobile robots is built by using the panel method known in fluid mechanics, which represents the outward normal velocity of each line of a polygonal obstacle as a function of the length of its characteristic line. The simulation results indicate that it is a simple, efficient and effective path planning algorithm for multiple mobile robots in the dynamic environments that the geometries and trajectories of obstacles are known in advance, and can achieve real-time performance.

scholarly journals Learning Innovative Routes for Mobile Robots in Dynamic Partially Unknown Environments

10.5772/5787 ◽  
2005 ◽  
Vol 2 (3) ◽  
pp. 21
Author(s):  
Kristo Heero ◽  
Alvo Aabloo ◽  
Maarja Kruusmaa
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Shortest Path ◽  
Dynamic Environments ◽  
Distance Transform ◽  
Test Results ◽  
Approach To Learning ◽  
Planning Strategy ◽  
Unknown Environments ◽  
Planning Strategies

This paper examines path planning strategies in partially unknown dynamic environemnts and introduces an approach to learning innovative routes. The approach is verified against shortest path planning with a distance transform algorithm, local and global replanning and suboptimal route following in unknown, partially unknown, static and dynamic environments. We show that the learned routes are more reliable and when traversed repeatedly the robot's behaviour becomes more predictable. The test results also suggest that the robot's behaviour depends on knowledge about the environemnt but not about the path planning strategy used.

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