Energy Efficient Communication with Lossless Data Encoding for Swarm Robot Coordination

Narayanan, K.; Honkote, Vinayak; Ghosh, Dibyendu; Baldev, Swamy

doi:10.1109/vlsid.2019.00118

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Within the computation of the EEA, the values for (3) are calculated over each iteration such that given for for (4) considering that…”

Section: A Extended Euclidean Algorithmmentioning

confidence: 99%

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An Efficient Homomorphic Data Encoding with Multiple Secret Hensel Codes

Silva¹,

Araujo²,

Chow³

2020

IJIEE

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“…Within the computation of the EEA, the values for (3) are calculated over each iteration such that given for for (4) considering that…”

Section: A Extended Euclidean Algorithmmentioning

confidence: 99%

“…Among its many applications, encoding techniques facilitate certain procedures with data such as storage and traffic [1], recovery [2], compression [3]- [5], signal processing [6], to cite a few. Classical examples of data encoding techniques include JPEG [7], MPEG [8], ASCII [9] and UTF-8 [10].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Homomorphic Data Encoding with Multiple Secret Hensel Codes

Silva¹,

Araujo²,

Chow³

2020

IJIEE

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“…Minimization of the energy consumption of a swarm as a whole is another important research area with core emphasis on a varied set of themes, such as dealing with external influences [ 23 , 24 ], optimization of consumption due to ranging sensors [ 25 ], efficient inter-robot communication [ 26 ], optimization of distance to be traveled [ 27 , 28 ], or recharging optimization [ 29 , 30 ]. In this respect, we present DCP-SLAM, a LiDAR-based distributed collaborative partial SLAM framework for swarm robotics.…”

Section: Introductionmentioning

confidence: 99%

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

Mahboob

Jokinen

Haghbayan

et al. 2023

Sensors

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Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments.

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“…Reducing energy consumption to increase mission life is another important research area in swarm robotics, focusing on a diverse set of topics, such as efficient decision making [20] , minimization of traveling distance [21] , energy efficient communication for swarm robot coordination [22] , decreasing the usage of ranging sensors [23] , and autonomous recharging [24] . In this paper, we present a novel approach to avoid congestion that may occur due to the overpopulation in either of the available gaps between the obstacles, resulting in delays and consequently higher energy consumption of the agents as well as the swarm as a whole.…”

Section: Introductionmentioning

confidence: 99%

Swarm formation morphing for congestion-aware collision avoidance

Haghbayan

Yasin

Plosila

2021

Heliyon

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The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the proposed approach results in substantial energy savings as compared with the traditional methods.

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Energy Efficient Communication with Lossless Data Encoding for Swarm Robot Coordination

Cited by 7 publications

References 3 publications

An Efficient Homomorphic Data Encoding with Multiple Secret Hensel Codes

An Efficient Homomorphic Data Encoding with Multiple Secret Hensel Codes

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

Swarm formation morphing for congestion-aware collision avoidance

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