Artificial intelligence algorithms in unmanned surface vessel task assignment and path planning: A survey

Gao, Kaizhou; Gao, Minglong; Zhou, Mengchu; Ma, Zhenfang

doi:10.1016/j.swevo.2024.101505

Cited by 3 publications

(1 citation statement)

References 127 publications

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“…The need for an enhanced monitoring strategy yielding complete datasets with higher spatial resolution has become critical, requiring an approach which would enable the prediction of water quality at the catchment scale and pave the way for its improvement [15][16][17][18][19]. Nowadays, innovative genetic algorithm-based equations can be employed as indices for comparing with observed data, yielding more realistic results compared to experimental data [20,21]. Another valuable tool is the application of satellite data, which provides high temporal and spatial coverage, although it is subject to specific timescales and parameters (e.g., chlorophyll α) with limitations based on weather conditions [22,23].…”

Section: Introductionmentioning

confidence: 99%

Use of Unmanned Surface Vehicles (USVs) in Water Chemistry Studies

Katsouras,

Dimitriou,

Karavoltsos

et al. 2024

Sensors

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Unmanned surface vehicles (USVs) equipped with integrated sensors are a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The elaboration of environmental monitoring programs has relied so far on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. In the present work, USVs were deployed by the Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyll α, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and the rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for the in situ measurements of copper and lead was also evaluated herewith. The results obtained demonstrate the advantages of USVs, setting the base for their application in real-time monitoring of chemical parameters including metals. Simultaneously, the requirements for accuracy and sensitivity improvement of HM sensors were noted, in order to permit full exploitation of USVs’ capacities.

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Section: Introductionmentioning

confidence: 99%

Use of Unmanned Surface Vehicles (USVs) in Water Chemistry Studies

Katsouras,

Dimitriou,

Karavoltsos

et al. 2024

Sensors

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A learning-based memetic algorithm for a cooperative task allocation problem of multiple unmanned aerial vehicles in smart agriculture

Chen,

Miao,

et al. 2024

Swarm and Evolutionary Computation

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Path planning for a UGV using Salp Swarm Algorithm

AlShabi,

Ballous,

Bou Nassif

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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The paper researches a utilization of the Salp Swarm Algorithm (SSA), a bio-mimetic optimization technique, to improve path planning in Unmanned Ground Vehicles (UGVs). Because of the crucial role of the efficient and reliable path planning in the implementation of UGVs in such sectors as military, rescue operations, and agriculture, there is a need for algorithms that are capable of navigating complex environments. The concept of SSA, based on the natural swarming behavior of salps, represents a very promising approach that is characterized by the exploration and exploitation properties of the algorithm. This study evaluates the performance of the SSA relative to existing particle swarm optimization (PSO), in terms of path optimality, computational efficiency, and dynamic obstacle adaptability, through a number of simulated environments. Results show that the SSA has the potential to compete with the traditional algorithms in path efficiency and computational load. However, PSO shows slight superiority results compared to SSA. This study highlights the potency of bio-inspired algorithms, specifically the SSA, in enhancing the field of autonomous navigation for UGVs. It introduces new possibilities of practical application of SSA in real-life scenarios, demonstrating its scalability and resilience. The findings of this study make a contribution to the general discussion on the improvement of planning of autonomous routes and provide a possible way for more sustainable and effective UGV activities.

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Artificial intelligence algorithms in unmanned surface vessel task assignment and path planning: A survey

Cited by 3 publications

References 127 publications

Use of Unmanned Surface Vehicles (USVs) in Water Chemistry Studies

Use of Unmanned Surface Vehicles (USVs) in Water Chemistry Studies

A learning-based memetic algorithm for a cooperative task allocation problem of multiple unmanned aerial vehicles in smart agriculture

Path planning for a UGV using Salp Swarm Algorithm

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