An innovative multi-agent search-and-rescue path planning approach

2015 ◽  
Vol 53 ◽  
pp. 24-31 ◽  
Author(s):  
Jean Berger ◽  
Nassirou Lo
Keyword(s):  
Path Planning ◽  
Search And Rescue ◽  
Planning Approach ◽  
Multi Agent

Some AI Based Approaches on Mobile Robots Motion Planning

2010 ◽  
Vol 166-167 ◽  
pp. 101-108 ◽  
Author(s):  
Adrian Burlacu ◽  
Marius Kloetzer ◽  
Doru Panescu
Keyword(s):  
Artificial Intelligence ◽  
Path Planning ◽  
Motion Planning ◽  
Mobile Robots ◽  
Artificial Vision ◽  
Temporal Logics ◽  
Artificial Intelligence Techniques ◽  
Planning Approach ◽  
Multi Agent

This contribution presents some important issues on mobile robots path planning. While it is hard to find a unique architecture for all the applications involving mobile robots, specific approaches can provide suitable solutions. Thus, three distinct structures are discussed, all making use of certain artificial intelligence techniques. They address the use and integration of artificial vision, a planning approach based on temporal logics, and a multi agent scheme. The three methods refer cases of mobile robots evolving in environments where various types of sensorial information can be obtained. Each of the proposed solutions determines advantages when it is used for a certain class of tasks.

scholarly journals R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

2021 ◽  
Vol 13 (8) ◽  
pp. 1525
Author(s):  
Gang Tang ◽  
Congqiang Tang ◽  
Hao Zhou ◽  
Christophe Claramunt ◽  
Shaoyang Men
Keyword(s):  
Path Planning ◽  
Google Earth ◽  
Simulation Experiments ◽  
Minimum Width ◽  
Coverage Path Planning ◽  
Optimal Paths ◽  
Planning Approach ◽  
Polygonal Area ◽  
Aerial Vehicle ◽  
Working Distance

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

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