A GA-ACO hybrid algorithm for the multi-UAV mission planning problem

Shang, Ke; Karungaru, Stephen; Feng, Zuren; Ke, Liangjun; Terada, Kenji

doi:10.1109/iscit.2014.7011909

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…Different algorithms have been developed to control the formation and reformation of a swarm of UAVs, which somewhat correspond to the team orienteering problem (TOP) [131]. In [131], a genetic algorithm and ant colony optimization hybrid algorithm (GA-ACO) is proposed to solve the multi-UAV mission planning.…”

Section: B Swarm Formationmentioning

confidence: 99%

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Shakeri

Al-Garadi

Badawy

et al. 2019

IEEE Commun. Surv. Tutorials

205

100

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Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. M 2 respectively. However, as shown in Fig.1, both technologies provide two extremes for surveillance with respect to accuracy versus intrusive deployment and maintenance. SRS provides slow large-scale monitoring, while CSNs require widespread deployment and constant human intervention for setup and maintenance, which may not be feasible particularly in harsh environments. On the one hand, SRS cannot be used for finegrain monitoring, such as monitoring of structural objects, with high accuracy, which limits its usage in many applications that require close monitoring and context detection [5]. On the other hand, traditional sensor networks (SNs) are spreading in countless number of Internet of Things (IoT) applications to provide automatic monitoring of objects, humans, and/or infrastructure. SNs, however, require permanent deployment of large magnitude of localized sensors, intrusively to the environment in many cases, and also require constant human intervention to manage and maintain. The proliferation of lowcost UAVs [6] is emerging to provide innovative ways of low altitude sensing with zero-deployment (i.e., no fixed deployment requirements) that can fundamentally change the way such applications are designed.Similar to surveillance applications, UAV technologies have progressed significantly in several applications in the past decade, making commercial and civil applications feasible and compelling. As it occurs with many disruptive technolo...

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Section: B Swarm Formationmentioning

confidence: 99%

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Shakeri

Al-Garadi

Badawy

et al. 2019

IEEE Commun. Surv. Tutorials

205

100

View full text Add to dashboard Cite

show abstract

“…[27] studies an optimal flying scheme in UAV assisted sensor networks and they implement GA approach to solve their multi-objective utility function against provided constraints. [32] deploys GA with ACO approach to provide a collaborative solution against their combinational optimization problem for multi-UAV flight planning with maximum surveillance. [33] also introduces an effective path planning scheme for mission specific aerial systems with GA implementation in a parallel architecture using multiple GPUs.…”

Section: Energy Efficiencymentioning

confidence: 99%

“…They apply momentum theory for energy consumption of the new configuration and assert that their model provide less energy consumption according to their evaluation.Algorithmic approaches considering energy model of a UAV also exist in the literature for energy awareness in UAV systems and they are mainly inspired for the thesis. They usually aim to accomplish a set of objectives with minimum energy consumption [32]. presents multi-UAV flight planning as an optimization problem to obtain the maximum presence as well as satisfying some constraints during the mission.…”

mentioning

confidence: 99%

Energy aware endurance framework for mission critical aerial networks

Özçevik

Canberk

2020

Ad Hoc Networks

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My colleagues were always kind to me and my directors always had a great understanding to allocate time for my PhD study. Fourthly, I would also like to acknowledge that BAP Unit of Istanbul Technical University supports my thesis with a project identified by the number: 41757. Lastly and foremost, I would like to thank my lovely family, especially to my wife Müge ÖZÇEVİK, her parents, my sister and my parents. I always felt their unending support in difficult times and I owed all of my achievements to them. In definite, I could not have achieved my current level of success without their support. Thanks for all.

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“…Then, improve the global searching solution [12,13] . ACO-PSO (Particle Swarm Optimization) is a method uses PSO to train the magnitude of ACO's parameters.…”

Section: Related Workmentioning

confidence: 99%

The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm

Zhao¹,

Yan²,

Ye³

2018

Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)

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Path planning in complex environment is the main foundation of flights automation navigation. Therefore, this paper aims to propose an algorithm combined ant colony optimal algorithm with potential field heuristic information. The main implement method of this algorithm is to use the information between the environment and goal to build the heuristic elements. What's more, the method defines this information as initial pheromone and converses elements to guide the ants to find the shortest path. Thanks to the heuristic elements in algorithm，the PFACO algorithm has a better searching tendency than traditional ACO algorithm，and overcome disadvantage in ants blindness. Through the simulation, the results show that PFACO has a better tendency of convergence. Compared with ACO, the PFACO algorithm can find a shorter path in same convergence time.

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A GA-ACO hybrid algorithm for the multi-UAV mission planning problem

Cited by 14 publications

References 13 publications

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Energy aware endurance framework for mission critical aerial networks

The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm

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