Flocking-Based Self-Organized Aggregation Behavior Method for Swarm Robotics

Mısır, Oğuz; Gökrem, Levent

doi:10.1007/s40998-021-00442-9

Cited by 10 publications

(3 citation statements)

References 57 publications

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“…Aggregation behaviors in groups of autonomous agents have been achieved using various paradigms, including Finite State Machines (FSMs) (Feola, Sion, Trianni, Reina, & Tuci, 2023;Firat, Ferrante, Gillet, & Tuci, 2020;Katada, 2018), novelty search (Gomes, Urbano, & Christensen, 2013;Hasselmann, Ligot, & Birattari, 2023) and flocking-based methods (Misir & Gökrem, 2021). However, one of the most widespread techniques involves the use of evolutionary algorithms (Bahgeçi & Sahin, 2005;Pagliuca & Vitanza, 2022, 2023aTrianni et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Exploring Behavioral Diversity in Robot Swarms: A Comparative Study of Evolutionary Strategies for Aggregation Tasks

Pagliuca,

Vitanza

2024

Preprint

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Collective decision-making is widely observed in natural organisms, especially insects and animals. In this regard, aggregation represents one of the paramount behaviors, as it can be useful for protecting groups against predators or speeding up the foraging process. In the field of autonomous robotics, aggregation is modeled through a series of alternative paradigms, among which evolutionary algorithms are considered a convenient tool. In this work, we compared three modern evolutionary strategies --- CMA-ES, xNES and OpenAI-ES --- for their ability to evolve an aggregation behavior in a swarm of robots. Specifically, we systematically varied the number of agents in the group, the environmental conditions (i.e., the number of target nests) and the parameters tuning the fitness function. Our aim is to verify whether and how the selected methods are effective at addressing the problem. The results we obtained indicate how the OpenAI-ES achieves better performance in all the considered scenarios. Furthermore, it displays qualitatively more interesting strategies than the other two methods.

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Section: Introductionmentioning

confidence: 99%

Exploring Behavioral Diversity in Robot Swarms: A Comparative Study of Evolutionary Strategies for Aggregation Tasks

Pagliuca,

Vitanza

2024

Preprint

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“…For use in the actual world without the assistance of a pilot, Ref. [ 29 ] developed a system that can automatically navigate and avoid obstacles. Moreover, Ref.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Choutri

Lagha

Meshoul

et al. 2022

Sensors

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Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

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“…At present, there are a series of key technology researches on swarm robots, including self-organizing behavior of swarm robots, path planning, network communication [1][2][3][4] . The path planning research of swarm robots is to find the shortest path from the starting point to the end point in a specific area, and collaborative communication between individuals in a swarm robot system is the key to positioning the swarm robot.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of swarm spherical robot based on machine vision

Huang

Yang²,

Chenghao³

et al. 2022

5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022)

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In this paper, a swarm spherical robot based on machine vision is proposed to adapt to the special applications of robots in the fields of land-based safety, navigation, surveillance and exploration, and discusses the spherical robot in terms of mechanism, control principles and positioning, respectively. The spherical robot structure is a telescopic low-degree-of-freedom parallel metamorphic mechanism, which is first analyzed mechanically using a modified Grubler-Kutzbach criterion to determine the degrees of freedom and to analyse the mechanics of key components. Secondly, the swarm robot consists of multiple spherical robots connected by magnetic forces. By designing a relevant path planning scheme to control the sequence of movements of these spherical robots, the movement of the whole swarm in the direction of a specific point can be achieved. Finally, the positioning and wireless control scheme of swarm robot based on machine vision is presented, and the motion principle of swarm robot is briefly introduced.

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Flocking-Based Self-Organized Aggregation Behavior Method for Swarm Robotics

Cited by 10 publications

References 57 publications

Exploring Behavioral Diversity in Robot Swarms: A Comparative Study of Evolutionary Strategies for Aggregation Tasks

Exploring Behavioral Diversity in Robot Swarms: A Comparative Study of Evolutionary Strategies for Aggregation Tasks

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Design and analysis of swarm spherical robot based on machine vision

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