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Hybrid Filtered Beam Search Algorithm for the Optimization of Monitoring Patrols

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Abstract

This paper offers an operational and methodological response for managing industrial risks by improving the monitoring of industrial areas. The objective is the optimization of monitoring patrols with automated mobile agents that are responsible for the surveillance. Such agents are formed by automated guided vehicles or unmanned aerial vehicles that carry various sensors. Apart from the specificities of each class of agents, the proposed approach is motivated by the need to inspect sites that may be dangerous or difficult to access. The optimization of the missions is carried out in compliance with functional (e.g., precedence of the operations) and operational (e.g., the travel time reserve of the agents) constraints in the double perspective of patrol configuration and trajectory planning as far as these aspects are strongly correlated. The questions that should be answered are as follows. How many mobile agents are required to perform a given set of measurements? How many sensors and what types of sensors must each of these agents equip? How to define the mission and trajectory of each agent? Such questions are studied as a multi-robots / multi-tasks problem, and an approach based on the hybrid filtered beam search is proposed for that purpose.

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Abbreviations

AGV:

Automated Guided Vehicles

IA:

Instantaneous Assignment

IP:

Integer Programming

MR:

Multi-Robot tasks

MT:

Multi-Task robots

MRTA:

Multi-Robot Task Allocation

MRS:

Multi-Robot Systems

SR:

Single-Robot

ST:

Single-Task

TA:

Time-extended Assignment

TSP:

Traveling Salesman Problem

UAV:

Unmanned Aerial Vehicles

VRP:

Vehicle Routing Problems

WSNs:

Wireless Sensor Networks

Agent x (S,r) :

Patrol S for the agents of type r in configuration x.

C(E,r) :

Travelling cost matrix

c e (a i, a j, M):

Elementary cost between two adjacent cells ai and aj for environment M

M 2D and G 2D :

Mesh and graph 3D for 2D environment

M 3D and G 3D :

Mesh and graph 3D for 3D environment

a i :

Site with one or more measurements

c (a i, a j, r):

Cost between two site ai and aj for an agent of type r

M(r,a i ) :

Set of measurements that an agent of type r can take in site ai

Sensor(r,m) :

Indicator that a robot of type r can take measurement m

S max (r) :

Maximal number of sensors of each agent of type r

T max (r) :

Travel time reserve of each agent of type r

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Funding

ANR-21-SIOM-0009 and Region Normandie, project APPRENTIS 2021–2023 “Formal APPRoach and artificial intElligeNce for the moniToring and Intervention optimization of mobile agents in industrial Sites”.

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Authors and Affiliations

  1. University le Havre Normandie, Le Havre, France

    Marwa Gam & Dimitri Lefebvre

  2. Laboratoire d’Ingenierie des Systèmes Industriels et des Energies Renouvelables, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, Tunis, Tunisia

    Achraf Jabeur Telmoudi

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  1. Marwa Gam
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Contributions

All authors contributed to the study, conception, and design. Coding, controller simulation, and data collection were performed by Marwa Gam. The first draft of the manuscript was written by Marwa Gam and Dimitri Lefebvre. All authors have assisted in editing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Marwa Gam.

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Gam, M., Telmoudi, A.J. & Lefebvre, D. Hybrid Filtered Beam Search Algorithm for the Optimization of Monitoring Patrols. J Intell Robot Syst 107, 26 (2023). https://doi.org/10.1007/s10846-022-01800-3

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