Research on Coverage Path Planning of Mobile Robot Based on Genetic Algorithm

2013 ◽  
Vol 819 ◽  
pp. 379-383 ◽  
Author(s):  
San Peng Deng ◽  
Zhong Min Wang ◽  
Peng Zhou ◽  
Hong Bing Wu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Cell Decomposition ◽  
Global Motion ◽  
Complete Coverage ◽  
Regional Environment ◽  
Coverage Path Planning ◽  
Environment Model ◽  
Local Space

This paper presents a complete coverage path planning method, which combines local space coverage with global motion planning. It is realized by modeling mobile robot environment based on Boustrophedon cell decomposition method; and according to the characteristics of regional environment model, the connectivity of the traversing space is represented by a complete weighted connected matrix. Then Genetic algorithm (GA) is used to optimize the subspace traversal distance to obtain the shortest global traversal sequence of mobile robot.

scholarly journals Complete Coverage Path Planning in an Unknown Underwater Environment Based on D-S Data Fusion Real-Time Map Building

2012 ◽  
Vol 8 (10) ◽  
pp. 567959 ◽  
Author(s):  
Mingzhong Yan ◽  
Daqi Zhu ◽  
Simon X. Yang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Evidence Theory ◽  
Sensor Data ◽  
Map Building ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Underwater Environment ◽  
Time Map

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

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