Patrolling task planning for the multi-layer multi-agent system based on sequential allocation method

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7896
Author(s):  
Jiyoun Moon

As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning in a broad range of areas by combining symbolic and connectionist approaches. The symbolic approach for understanding and learning human knowledge is useful for task planning in a wide and static environment. The network-based connectionist approach has the advantage of being able to respond to an ever-changing dynamic environment. A planning domain definition language-based planning algorithm, which is a symbolic approach, and the cooperative–competitive reinforcement learning algorithm, which is a connectionist approach, were utilized in this study. The proposed architecture is verified through a simulation. It is also verified through an experiment using 10 unmanned surface vehicles that the given tasks were successfully executed in a wide and dynamic environment.


2009 ◽  
Vol 2 (4) ◽  
pp. 61-70
Author(s):  
Ravi Babu Pallikonda ◽  
◽  
K. Prapoorna ◽  
N.V. Prashanth ◽  
A. Shruti ◽  
...  

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