A cloud based autonomous multipurpose system with self-communicating bots and swarm of drones

Saha, Himadri Nath; Das, Nishith Kumar; Pal, S. K.; Basu, Srijita; Auddy, Supratim; Dey, Ratul; Nandy, Arnab; Pal, Debjit; Roy, Nirjhar; Mitra, Dipanjan; Biswas, Saunak; Maity, Tamanna

doi:10.1109/ccwc.2018.8301781

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…Our analysis excluded 42.37% of the articles from the total aggregate gathered from the two queries. Among these, we identify articles such as [31], [32], [33]. EC3: Does not show one or more types of interaction.…”

Section: Third Stage: Selecting Relevant Articlesmentioning

confidence: 99%

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

Şiean¹,

Gradinaru²,

Gherman³

et al. 2023

IJACSA

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We conducted a Systematic Literature Review on scientific papers that examined the interaction between operators and drone swarms based on the use of a command and control center. We present the results of a meta-analysis of nine scientific papers published in the ACM DL and IEEE Xplore databases. Our findings show that research on human-drone swarm interaction shows a disproportionate interest in hand gestures compared to other input modalities for drone swarm control. Furthermore, all articles reviewed exclusively explored gestures and the size of the swarm used in the studies was limited, with a median of 3.0 and an average of 3.8 drones per study. We compiled an inventory of interaction modalities, recognition techniques, and application types from the scientific literature, which is presented in this paper. On the basis of our findings, we propose four areas for future research that can guide scientific investigations and practical developments in this field.

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Section: Third Stage: Selecting Relevant Articlesmentioning

confidence: 99%

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

Şiean¹,

Gradinaru²,

Gherman³

et al. 2023

IJACSA

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“…With the help of artificial intelligence, suitable algorithms are being developed to provide efficient controls over swarms of drones as exemplified by Shrit et al [22]. A swarm intelligence-based design with specific communication among systems of drones and bot clusters was proposed by Saha et al [45]. A master drone fetches the sensor information from the cloud upon request, thereby achieving coordination between ground and sky systems.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Artificial intelligence, navigation strategies, and cryptography have been integrated into UAV communication techniques by various researchers to maintain efficient, reliable, and low-latency communications between nodes of the UAV network [11,12,22,28,29,[32][33][34][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]. However, it is important to consider energy efficiency, as well as the speed of drones for reliable secure communication.…”

Section: Introductionmentioning

confidence: 99%

Communication and networking technologies for UAVs: A survey

Sharma

Vanjani

Paliwal

et al. 2020

Journal of Network and Computer Applications

238

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With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods.Encryption and optimization techniques for ensuring long−lasting and secure communications, as well as for power management, are discussed.Moreover, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra-reliable and low−latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications.

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“…SR is an alternative method for coordinating systems with several robots that use a large number of autonomous nodes [2]. Drone's interactions with one another and with their surroundings lead to the emergence of collective behaviour [3]. The main idea behind this domain research strategy is to build a large number of tiny low-budget robots that are expected to do the same job as a single complex robot or a small group of complex robots.…”

Section: Introductionmentioning

confidence: 99%

Towards Reliable Identification and Tracking of Drones Within a Swarm

Kumari,

Lee,

Barca

et al. 2024

J Intell Robot Syst

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Drone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm.

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A cloud based autonomous multipurpose system with self-communicating bots and swarm of drones

Cited by 12 publications

References 13 publications

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

Communication and networking technologies for UAVs: A survey

Towards Reliable Identification and Tracking of Drones Within a Swarm

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