The TAM: abstracting complex tasks in swarm robotics research

Swarm robotics carries out complex tasks beyond the power of simple individual robots. Limited capabilities of sensing and communication by simple mobile robots have been essential inspirations for aggregation tasks. Aggregation is crucial behavior when performing complex tasks in swarm robotics systems. Many difficulties are facing the aggregation algorithm. These difficulties are as such: this algorithm has to work under the restrictions of no information about positions, no central control, and only local information interaction among robots. This paper proposed a new aggregation algorithm. This algorithm combined with the wave algorithm to achieve collective navigation and the recruitment strategy. In this work, the aggregation algorithm consists of two main phases: the searching phase, and the surrounding phase. The execution time of the proposed algorithm was analyzed. The experimental results showed that the aggregation time in the proposed algorithm was significantly reduced by 41% compared to other algorithms in the literature. Moreover, we analyzed our results using a one-way analysis of variance. Also, our results showed that the increasing swarm size significantly improved the performance of the group.

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A Study on Various Task-Work Allocation Algorithms in Swarm Robotics

Journal of Information Technology and Digital World - September 2019 ◽

10.36548/jitdw.2020.2.006 ◽

2020 ◽

Vol 2 (2) ◽

pp. 123-134

Author(s):

Annamalai .L ◽

Mohammed Siddiq. M ◽

Ravi Shankar. S ◽

Vigneshwar .S

Keyword(s):

Mobile Robots ◽

Swarm Intelligence ◽

Task Allocation ◽

Swarm Robotics ◽

Entire System ◽

Complex Tasks ◽

Work Allocation ◽

Allocation Algorithms

This paper discusses the various task allocation algorithms that have been researched, analyzed, and used in swarm robotics. The main reason for switching over to swarm robotics from ordinary mobile robots is because of its ability to perform complex tasks co-operatively with other bots rather than individually. Furthermore, they can be scaled to perform any kind of tasks. To carry out tasks like foraging, surveying and other such tasks that require swarm intelligence, task allocation plays an important role. It is the crux of the entire system and plays a huge role in the success of the implementation of swarm robotics. Few algorithms that address this task allocation have been briefly discussed here.

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An Introduction to Swarm Robotics

ISRN Robotics ◽

10.5402/2013/608164 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 69

Author(s):

Iñaki Navarro ◽

Fernando Matía

Keyword(s):

Social Insects ◽

Real World ◽

Swarm Robotics ◽

Experimental Results ◽

Robotic Systems ◽

Complex Tasks ◽

Real World Applications ◽

The Future

Swarm robotics is a field of multi-robotics in which large number of robots are coordinated in a distributed and decentralised way. It is based on the use of local rules, and simple robots compared to the complexity of the task to achieve, and inspired by social insects. Large number of simple robots can perform complex tasks in a more efficient way than a single robot, giving robustness and flexibility to the group. In this article, an overview of swarm robotics is given, describing its main properties and characteristics and comparing it to general multi-robotic systems. A review of different research works and experimental results, together with a discussion of the future swarm robotics in real world applications completes this work.

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Enhancing Learning, Performance, and Adaptability for Complex Tasks

PsycEXTRA Dataset ◽

10.1037/e423442005-001 ◽

2005 ◽

Author(s):

Steve W. J. Kozlowski ◽

◽

Richard P. DeShon

Keyword(s):

Learning Performance ◽

Complex Tasks

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Role of Observational Learning in Automated Instruction of Complex Tasks

PsycEXTRA Dataset ◽

10.1037/e443792005-001 ◽

1998 ◽

Author(s):

Barry P. Goettl ◽

Cathy Connolly-Gomez

Keyword(s):

Observational Learning ◽

Complex Tasks

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The prototypes of nanozyme-based nanorobots

Biophysics Reports ◽

10.1007/s41048-020-00125-8 ◽

2020 ◽

Vol 6 (6) ◽

pp. 223-244

Author(s):

Jiaying Xie ◽

Yiliang Jin ◽

Kelong Fan ◽

Xiyun Yan

Keyword(s):

Functional Nanomaterials ◽

Promising Candidate ◽

Future Directions ◽

Complex Tasks ◽

Logic Control ◽

The Future ◽

Flexible Designs

AbstractArtificial nanorobot is a type of robots designed for executing complex tasks at nanoscale. The nanorobot system is typically consisted of four systems, including logic control, driving, sensing and functioning. Considering the subtle structure and complex functionality of nanorobot, the manufacture of nanorobots requires designable, controllable and multi-functional nanomaterials. Here, we propose that nanozyme is a promising candidate for fabricating nanorobots due to its unique properties, including flexible designs, controllable enzyme-like activities, and nano-sized physicochemical characters. Nanozymes may participate in one system or even combine several systems of nanorobots. In this review, we summarize the advances on nanozyme-based systems for fabricating nanorobots, and prospect the future directions of nanozyme for constructing nanorobots. We hope that the unique properties of nanozymes will provide novel ideas for designing and fabricating nanorobotics.

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

Iranian Journal of Science and Technology Transactions of Electrical Engineering ◽

10.1007/s40998-021-00442-9 ◽

2021 ◽

Author(s):

Oğuz Misir ◽

Levent Gökrem

Keyword(s):

Swarm Robotics ◽

Aggregation Behavior ◽

Self Organized

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Effect of Environmental Enrichment on the Brain and on Learning and Cognition by Animals

Animals ◽

10.3390/ani11040973 ◽

2021 ◽

Vol 11 (4) ◽

pp. 973

Author(s):

Thomas R. Zentall

Keyword(s):

Cognitive Abilities ◽

Environmental Enrichment ◽

Behavioral Effect ◽

Enriched Environment ◽

Main Concern ◽

Accurate Assessment ◽

Housing Conditions ◽

Complex Tasks ◽

Learning And Cognition ◽

The Brain

The humane treatment of animals suggests that they should be housed in an environment that is rich in stimulation and allows for varied activities. However, even if one’s main concern is an accurate assessment of their learning and cognitive abilities, housing them in an enriched environment can have an important effect on the assessment of those abilities. Research has found that the development of the brain of animals is significantly affected by the environment in which they live. Not surprisingly, their ability to learn both simple and complex tasks is affected by even modest time spent in an enriched environment. In particular, animals that are housed in an enriched environment are less impulsive and make more optimal choices than animals housed in isolation. Even the way that they judge the passage of time is affected by their housing conditions. Some researchers have even suggested that exposing animals to an enriched environment can make them more “optimistic” in how they treat ambiguous stimuli. Whether that behavioral effect reflects the subtlety of differences in optimism/pessimism or something simpler, like differences in motivation, incentive, discriminability, or neophobia, it is clear that the conditions of housing can have an important effect on the learning and cognition of animals.

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