A Clustering Market-Based Approach for Multi-robot Emergency Response Applications

Trigui, Sahar; Koubâa, Anis; Cheikhrouhou, Omar; Qureshi, Basit; Youssef, Habib

doi:10.1109/icarsc.2016.14

Cited by 12 publications

(8 citation statements)

References 10 publications

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“…The mTSP is of practical significance because it can be used to solve many everyday problems. Examples of the solved problems that have been reported in the literature include the printing-press scheduling problem (Carter & Ragsdale, 2006), the planning of autonomous mobile robots (Elango, Nachiappan & Tiwari, 2011;Trigui & Koub, 2016;Yu, Jinhai, Guochang, Rubo & Haiyan, 2002), unmanned aerial vehicles (Ann, Kim & Ahn, 2015;Lu, Zhang, He & Niu, 2016), production scheduling (Tang, Liu, Rong & Yang, 2000), designing networks for satellite systems (Boone, Sathyan & Cohen, 2015;Saleh & Chelouah, 2004), optimizing energy consumption in wireless sensor networks (Ma, Shi & Gu, 2018), routing vehicles (Expósito-izquierdo, Rossi & Sevaux, 2016) and drone deliveries (Kitjacharoenchai, Ventresca, Moshref-Javadi, Lee, Tanchoco & Brunese, 2019).…”

Section: The Contextmentioning

confidence: 99%

A novel clustering method for breaking down the symmetric multiple traveling salesman problem

Hamdan

Bashir

Cheaitou

2021

JIEM

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Purpose: This study proposes a new two-stage clustering method to break down the symmetric multiple traveling salesman problem (mTSP) into several single standard traveling salesman problems, each of which can then be solved separately using a heuristic optimization algorithm.Design/methodology/approach: In the initial stage, a modified form of factor analysis is used to identify clusters of cities. In the second stage, the cities are allocated to the identified clusters using an integer-programming model. A comparison with the k-means++ clustering algorithm, one of the most popular clustering algorithms, was made to evaluate the performance of the proposed method in terms of four objective criteria.Findings: Computational results and comparison on 63 problems revealed that the proposed method is promising for producing quality clusters and thus for enhancing the performance of heuristic optimization algorithms in solving the mTSP.Originality/value: Unlike previous studies, this study tackles the issue of improving the performance of clustering-based optimization approaches in solving the mTSP by proposing a new clustering method that produces better cluster solutions rather than by proposing a new or improved version of a heuristic optimization algorithm for finding optimal routes.

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Section: The Contextmentioning

confidence: 99%

A novel clustering method for breaking down the symmetric multiple traveling salesman problem

Hamdan

Bashir

Cheaitou

2021

JIEM

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“…The proposed AHP-MTSP algorithm was designed to select the best result from three approaches namely the market-based approach, the RTMA approach and the balanced. However, for lack of space, we present here only the market-based approach and we refer the reader to paper [67][68][69] for more details.…”

Section: Optimized Rescue Plan Developmentmentioning

confidence: 99%

A Cloud Based Disaster Management System

Cheikhrouhou

Koubâa

Zarrad

2020

JSAN

Self Cite

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The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.

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“…Otto et al [28] reviewed the literature on civil aircraft optimization methods for UAVs. Similarly, Trigui et al [29] applied a traveling salesman problem (TSP) variant model to solve the multi-UAV emergencyresponse problem for multiple potential targets. Multiple objectives, including total trips, maximum trips, and mission time, can be optimized using the model.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Rapid-Assessment Task-Assignment Problem in a Post-Earthquake Scenario

Zhu

Zhang

et al. 2019

IEEE Access

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The rapid assessment of earthquake-stricken regions immediately after a seismic event is crucial for earthquake relief operations. Since unmanned aerial vehicles (UAVs) can quickly reach the affected areas and obtain images, they are widely used in the post-earthquake rapid assessment. However, sensor noise and other unavoidable errors can affect the quality of images acquired by sensors attached to the UAVs, which can, in turn, reduce the quality of the assessment. We defined a new problem in the application of multiple UAVs in the rapid assessment of earthquake-stricken regions. The rapid-assessment task-assignment problem (RATAP) was used to construct the assignment plan for multiple UAVs in a rapidassessment task while considering the weights of potential targets, the endurance of the UAVs, and the sensor errors. The RATAP was formulated as a variant of the team orienteering problem (TOP) called the revisitallowed TOP with reward probability (RTOP-RP). We then developed an efficient hybrid particle swarm optimization with simulated annealing (HPSO-SA) algorithm, which produced a high-quality solution for the RATAP, and confirmed the effectiveness and rapidity of our algorithm through numerical experiments. Finally, we conducted a case study based on real-world data from the 2008 Wenchuan earthquake in China to demonstrate our approach. INDEX TERMS Post-earthquake, multiple unmanned aerial vehicles, rapid-assessment task-assignment problem, target-revisit-allowed strategy.

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A Clustering Market-Based Approach for Multi-robot Emergency Response Applications

Cited by 12 publications

References 10 publications

A novel clustering method for breaking down the symmetric multiple traveling salesman problem

A novel clustering method for breaking down the symmetric multiple traveling salesman problem

A Cloud Based Disaster Management System

Multi-UAV Rapid-Assessment Task-Assignment Problem in a Post-Earthquake Scenario

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