Mapping and coverage with a particle swarm controlled by uniform inputs

Mahadev, Arun; Krupke, Dominik; Fekete, Sándor P.; Becker, Aaron T.

doi:10.1109/iros.2017.8202280

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…In this context it becomes difficult to abstract properties and reuse results across different works. However, by some mathematical abstraction, it is still possible to identify and perform an analysis of typical classes of swarm behaviors that include, among others, foraging [79,80,81,82], coverage [83,84,85,86], aggregation and pattern formation [87,88,89,80], and cooperative tracking [83,90,91] (an extensive, and well discussed list of swarm behaviors can be found in [92,93], and an extension in [13]).…”

Section: Challenges Deriving Formal Propertiesmentioning

confidence: 99%

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Schranz

Schmickl

et al. 2021

Swarm and Evolutionary Computation

131

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Challenges Deriving Formal Propertiesmentioning

confidence: 99%

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Schranz

Schmickl

et al. 2021

Swarm and Evolutionary Computation

131

View full text Add to dashboard Cite

show abstract

“…The results of this section highlight a broad spectrum of joint work with Victor Baez, Aaron Becker, Erik Demaine, Golnaz Habibi, Li Huang, Phillip Keldenich, Linda Kleist, Dominik Krupke, Jarrett Lonsford, Arun Mahadev, Sheryl Manzoor, James McLurkin, Rose Morris-Wright, Hamed Mohtasham Shad, Christian Rieck, Christian Scheffer, and Arne Schmidt; see [3,4,[6][7][8]10,20,23,24,26,27] for further details. The shown video [2] is available at https://www.…”

Section: Uniform Global Control For Particle Swarmsmentioning

confidence: 99%

Coordinating Swarms of Objects at Extreme Dimensions

Fekete

2020

Lecture Notes in Computer Science

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We describe a variety of algorithmic challenges arising from coordination and reconfiguration of swarms of potentially many objects, ranging in size from minuscule particles all the way to far-away satellite swarms. Particular results include methods for coordinating the motion of vehicles in traffic in order to avoid inefficient stop-and-go congestions; using uniform global forces for controlling particle swarms; online triangulation and structured exploration; cohesive control for swarms of robots with only local communication; coordinated motion planning for efficiently reconfiguring an arrangement of robots; and constructing and reconfiguring large-scale structures by finite automata. All presented work is based on collaborations with a variety of authors, who are named in the respective sections of this overview.

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“…Other researchers have attempted to control this type of underactuated system by adding extra constrains. These include placing obstacles in the workspace [7], [8], providing non-slip boundary contacts [9], [10], changing the physical shape of the robot [11], and applying an external artificial force field [12]. When there is a large number of the robots, it can be difficult to detect their individual positions, however Shahrokhi et al showed that "it is possible to sense global properties such as mean position and variance" [3].…”

Section: Introductionmentioning

confidence: 99%

Swarm Control of Magnetically Actuated Millirobots

Razzaghi¹,

Khatib²,

Hürmüzlü³

2021

Preprint

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Small-size robots offer access to spaces that are inaccessible to larger ones. This type of access is crucial in applications such as drug delivery, environmental detection, and collection of small samples. However, there are some tasks that are not possible to perform using only one robot including assembly and manufacturing at small scales, manipulation of micro-and nano-objects, and robot-based structuring of smallscale materials. The solution to this problem is to use a group of robots as a swarm system. Thus, in this article, we focus on tasks that can be achieved using a group of small-scale robots.These robots are typically externally actuated due to their size limitation. Yet, one faces the challenge of controlling a group of robots using a single global input. In this study, we propose a control algorithm to position individual members of a swarm in predefined positions. A single control input applies to the system and moves all robots in the same direction. We also add another control modality by using different length robots.In our previous work [1], we developed a small-scaled magnetically actuated millirobot. An electromagnetic coil system applied external force and steered the millirobots. This millirobot can move in various modes of motion such as pivot walking and tumbling. In this paper, we propose two new designs of these millirobots. In the first design, the magnets are placed at the center of body to reduce the magnetic attraction force between the millirobots. In the second design, the millirobots are of identical length with two extra legs acting as the pivot points. This way we vary pivot separation in design to take advantage of variable speed in pivot walking mode while keeping the speed constant in tumbling mode.This paper presents a general algorithm for positional control of n millirobots with different lengths to move them from given initial positions to final desired ones. This method is based on choosing a leader that is fully controllable. Then, the motions of a group of follower millirobots are regulated by following the leader and determining their appropriate pivot separations in order to implement the intended swarm motion. Simulations and hardware experiments validate these results.

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Mapping and coverage with a particle swarm controlled by uniform inputs

Cited by 8 publications

References 32 publications

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Coordinating Swarms of Objects at Extreme Dimensions

Swarm Control of Magnetically Actuated Millirobots

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