Decentralized Bioinspired Non-Discrete Model for Autonomous Swarm Aggregation Dynamics

Round 1

Reviewer 1 Report

In this paper, a mathematical model is introduced to predict one kind of biological behavior--nodal aggregation dynamics of robotic swarms. There are several comments should be pay attention:

(1)The expression should be accurate. 2.2 task. Vectors and points are two different things and should not be confused. The center should be a point instead of '3-vector'. Others should be revised also, for example, rn...

(2) Figure 3, the legends should be adjusted and visible. 

(3) 3.1 Assumptions. 'Unit square' what unit used here.

(4) Explain figure 6,7 in detail including the trend of the color changed.

(5) Give an estimation of the error resulting from the model used here.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

I believe that the article is of significant interest to specialists in the field of swarm robotics. From the point of view of practice, the question raises only too many nodes with which the authors experimented. It would be interesting to see how the proposed method works with a small number of nodes. I think that this work has a very high development potential.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

The paper “Decentralized bioinspired nondiscrete model for autonomous swarm aggregation dynamics” by Oikonomou and Pappas proposes a microscopic, non-discrete, mathematical model based on stigmergy for predicting the nodal aggregation dynamics of decentralized, autonomous robotic swarms.

Authors developed time-continuous simulations where nodal aggregation efficiency was evaluated using

time to aggregation equilibrium, agent spatial distribution within aggregate and deviation from target agent number. Authors observed that optimization using Linear Multivariable Polynomial Regression and Random Forest Regression to obtain the best fit of the generated data set, provided similar results.

The proposed optimized model describes the physical properties that non-digital agents must possess so that the proposed aggregation behavior emerges, in order to avoid discrete state algorithms aiming towards developing agents independent of digital components aiding to their miniaturization.

 

The paper is rather interesting but some issues should be addressed by authors in order to have a paper suitable for publication.

 

Pag. 2: “This behavior is commonly observed in nature with biological systems such as bee swarms and cockroach pools under light spots”, an important aspect authors did not included is that aggregations are flexible self-organized behaviors, where the coalescence of the group is increased by the presence of a stress (e.g. a predator) and decreased when limited resources trigger competition. You should expand this part. Maybe authors can appreciate to include these works to improve their manuscript:

Hoare D J, Couzin I D, Godin J G, Krause J. Context- dependent group size choice in fish. Animal Behaviour, 2004, 67, 155–164.

Romano, D., Elayan, H., Benelli, G., & Stefanini, C. (2020). Together We Stand–Analyzing Schooling Behavior in Naive Newborn Guppies through Biorobotic Predators. Journal of Bionic Engineering, 17(1), 174-184.

Hamilton, W. D. (1971). Geometry for the selfish herd. Journal of Theoretical Biology, 31(2), 295-311.

 

Legends in the figures are not clearly visible.

 

The text should be strongly improved as it is very hard to read.

Author Response

Please see the attachment.

Author Response File: Author Response.docx