Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

Wu, Gongxing; Xu, Tao; Sun, Yushan; Zhang, Jiawei

doi:10.1177/17298806221091885

Cited by 29 publications

(9 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ACO beats the other studied algorithms with respect to of convergence, which refers robustness, and parallelism when applying fundamental mathematical operations, according to Wu et al 22 It is, nevertheless, quite easy to fall into local optima. The route planning of driverless surface vehicles attempts to use optimization algorithms to estimate the trajectories for a given activity.…”

Section: Introductionmentioning

confidence: 91%

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Kumaragurubaran,

Vijayakumar

2024

Int J Communication

View full text Add to dashboard Cite

SummaryA vehicular ad hoc network (VANET) is a wireless network composed of a collection of mobility devices that link via a wireless channel without the need for permanent infrastructure or centralized administration. Multicasting is a method of sending data from a single node to multiple recipients at the same time. Because of its energizing network topology and constricted resources, a mobile ad hoc network (MANET) presents a number of issues. Because network execution measurements such as latency, bandwidth, throughput, and energy fluctuate so rapidly owing to node movement, these network properties in a wireless mobile ad hoc network are ambiguous. Because of the influence of these uncertainty issues, determining the top‐grade path from the originating node to a set of recipient nodes is challenging. In order to preserve network resources, we used a Swarm Intelligence‐based fuzzy logic strategy in this study that includes channel caliber factor and connection termination time to try to alleviate these uncertainty issues. This strategy integrates all of the network indicators available for the routes into an individual quantity named as fuzzy cost or cost of communication. The routes with the lowest fuzzy cost will be regarded optimum, and data will be delivered along this route from the originating point to a set of receivers. The simulation was carried out using NS‐3.38 and MATLAB, and the results reveal that the suggested protocol, HACOFLM, outperforms ODMRP, OFAODV, MAODV, and DFMCRP by means of packet delivery ratio, latency, control packet overhead, and throughput. DFMCRP outperforms MAODV by 3% to 6%, OFAODV by 13% to 19%, and ODMRP by 16% to 23%. According to HACOSMO, based on the number of vehicles and velocities, routing overhead is 7% to 10% less than DFMCRP, 9% to 13% less than MAODV, 17% to 29% less than OFAODV, and 22% to 36% less than ODMRP. It improvise throughput by 4% to 6% when contrasted to DFMCRP, 5% to 9% when contrasted to MAODV, 10% to 14% when contrasted to OFAODV, and 14% to 23% when contrasted to ODMRP.

show abstract

Section: Introductionmentioning

confidence: 91%

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Kumaragurubaran,

Vijayakumar

2024

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Depending on the mission and the sensors and instruments installed on the vehicle, UUVs can collect a variety of data such as bathymetry, water quality, imagery of the seafloor, and other types [28,31,32,36,41,42,49].…”

Section: Rq1mentioning

confidence: 99%

“…To move through water, UUVs use navigation and control systems. An inertial navigation system, a GPS system, and a sonar system are a few examples [20,21,23,28,37,43,44,54,55,59].…”

Section: Rq2mentioning

confidence: 99%

“…UUVs can be equipped with various payloads to perform specific tasks such as sampling, imaging, and mapping [23,28,32,44,49,54,55,59].…”

Section: Rq5mentioning

confidence: 99%

“…Fourth, adaptive control (AC) uses a mathematical model of the system to estimate the current state of the system [26] and then adjusts the control inputs accordingly. Last but not least, adjusting UUV trajectory [27] or UUV herd localization uses Artificial Intelligence and Machine Learning (ML) [28]. Control algorithms are chosen based on the application and the requirements given, but there are a number of testing models that can be used, including (1) simulation; (2) closed-loop testing;…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Unmanned Underwater Vehicles: Challenges, Enabling Technologies, and Future Research Directions

Wibisono,

Piran,

Song

et al. 2023

Sensors

View full text Add to dashboard Cite

Unmanned underwater vehicles (UUVs) are becoming increasingly important for a variety of applications, including ocean exploration, mine detection, and military surveillance. This paper aims to provide a comprehensive examination of the technologies that enable the operation of UUVs. We begin by introducing various types of unmanned vehicles capable of functioning in diverse environments. Subsequently, we delve into the underlying technologies necessary for unmanned vehicles operating in underwater environments. These technologies encompass communication, propulsion, dive systems, control systems, sensing, localization, energy resources, and supply. We also address general technical approaches and research contributions within this domain. Furthermore, we present a comprehensive overview of related work, survey methodologies employed, research inquiries, statistical trends, relevant keywords, and supporting articles that substantiate both broad and specific assertions. Expanding on this, we provide a detailed and coherent explanation of the operational framework of UUVs and their corresponding supporting technologies, with an emphasis on technical descriptions. We then evaluate the existing gaps in the performance of supporting technologies and explore the recent challenges associated with implementing the Thorp model for the distribution of shared resources, specifically in communication and energy domains. We also address the joint design of operations involving unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and UUVs, which necessitate collaborative research endeavors to accomplish mission objectives. This analysis highlights the need for future research efforts in these areas. Finally, we outline several critical research questions that warrant exploration in future studies.

show abstract

Strategy Determination for Multiple USVs: A Min-max Q-learning Approach

Hong,

Cui

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

Cited by 29 publications

References 70 publications

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

A Survey on Unmanned Underwater Vehicles: Challenges, Enabling Technologies, and Future Research Directions

Strategy Determination for Multiple USVs: A Min-max Q-learning Approach

Contact Info

Product

Resources

About