A Task Decomposition Using (HDec-POSMDPs) Approach for Multi-Robot Exploration and Fire Searching
In this paper, a hierarchical control architecture for coordinated Multi-robot systems (MRS) task decomposi-tion is presented based on a hybrid decentralized Partially Observable Semi-Markov Decision Processes (HDec-POSMDPs) to perform the exploration of an unknown environment and search for the cluttered fire source in this environment. In this architecture, robots can make their own decisions according to their locally collected data with limited communication between a robot team. The overall task decomposes into multiple local sub-tasks, each sub-task is described as a set of regular languages. MRS are modeled as a discrete event system and each robot is represented by a deterministic finite state automaton model. The proposed approach is implemented and evaluated using software simulator. By using this architecture, the task execution time is minimized, the fire sources cluttered in an environment have been searched in an effective manner and the per-formance of MRS has been enhanced with respect to energy consumption and communication load; when they are used for exploring different environments as well as when they are used for detecting the sources of the fire and reporting about them.
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