Abstract
This paper addresses the problem of multi-robot task allocation and trajectory planning in industrial environments. The objective is to optimize the overall cost of robot surveillance patrols in a dynamic high-risk environment. In this context, a hybrid beam search based approach is proposed to plan the patrol trajectories iteratively to accommodate environmental changes under some functional and operational constraints. Moreover, a real-time based system is introduced for remotely monitoring dynamic surveillance missions with automated mobile agents. Finally, a case study is detailed to show the efficiency of our approach in the case of the industrial port area of Fos-sur-Mer city in France.
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