Individuality in Swarm Robots with the Case Study of Kilobots: Noise, Bug, or Feature?

Inter-individual differences are studied in natural systems, such as fish, bees, and humans, as they contribute to the complexity of both individual and collective behaviors. However, individuality in artificial systems, such as robotic swarms, is undervalued or even overlooked. Agent-specific deviations from the norm in swarm robotics are usually understood as mere noise that can be minimized, for example, by calibration. We observe that robots have consistent deviations and argue that awareness and knowledge of these can be exploited to serve a task. We measure heterogeneity in robot swarms caused by individual differences in how robots act, sense, and oscillate. Our use case is Kilobots and we provide example behaviors where the performance of robots varies depending on individual differences. We show a non-intuitive example of phototaxis with Kilobots where the non-calibrated Kilobots show better performance than the calibrated supposedly ``ideal" one. We measure the inter-individual variations for heterogeneity in sensing and oscillation, too. We briefly discuss how these variations can enhance the complexity of collective behaviors. We suggest that by recognizing and exploring this new perspective on individuality, and hence diversity, in robotic swarms, we can gain a deeper understanding of these systems and potentially unlock new possibilities for their design and implementation of applications.Comment: Accepted at the 2023 Conference on Artificial Life (ALife). To see the 9 Figures in large check this repo: https://github.com/mohsen-raoufi/Kilobots-Individuality-ALife-23/tree/main/Figure

Self-organized Collective Motion with a Simulated Real Robot Swarm

Collective motion is one of the most fascinating phenomena observed in the nature. In the last decade, it aroused so much attention in physics, control and robotics fields. In particular, many studies have been done in swarm robotics related to collective motion, also called flocking. In most of these studies, robots use orientation and proximity of their neighbors to achieve collective motion. In such an approach, one of the biggest problems is to measure orientation information using on-board sensors. In most of the studies, this information is either simulated or implemented using communication. In this paper, to the best of our knowledge, we implemented a fully autonomous coordinated motion without alignment using very simple Mona robots. We used an approach based on Active Elastic Sheet (AES) method. We modified the method and added the capability to enable the swarm to move toward a desired direction and rotate about an arbitrary point. The parameters of the modified method are optimized using TCACS optimization algorithm. We tested our approach in different settings using Matlab and Webots

An alternate method to extract performance characteristics in dye sensitized solar cells

Modeling the electrical properties of dye-sensitized solar cells (DSSCs) can fill the gap between the experimental and ideal performance observations for a reliable device diagnosis, design and optimization. The complex physical and chemical reactions between nanocrystalline semiconductor, electrolyte ions and dye molecules make their simulation an open issue to the researchers. Compared to the research works presented in literature, here, we provide a simpler, but more meaningful fit of current voltage curves by developing a simulation model. The present work provides a reliable framework to extract electrical transport properties of the device, namely, diffusion coefficient, transport time, diffusion length, series resistance and performance parameters from steady state current voltage curves without using interpretable frequency dependent methods, as well as transient characteristics. The model versatility makes it also capable of predicting the dye regeneration efficiency under short circuit, mid-voltages and high voltage ranges. The simulation method can be also implemented to compare the effect of different electrolytes as well as their species concentrations on regeneration efficiency and overall DSSCs performance. The whole model is designed in a flexible framework to be adapted to various kind of solar cells such as quantum dot and perovskite solar cells. (C) 2017 Elsevier GmbH. All rights reserved

A self-adaptive landmark-based aggregation method for robot swarms

Aggregation, a widely observed behavior in social insects, is the gathering of individuals on any location or on a cue. The former being called the self-organized aggregation, and the latter being called the cue-based aggregation. One of the fascinating examples of cue-based aggregation is the thermotactic behavior of young honeybees. Young honeybees aggregate on optimal temperature zones in the hive using a simple set of behaviors. The state-of-the-art cue-based aggregation method BEECLUST was derived based on these behaviors. The BEECLUST method is a very simple, yet a very capable method that has favorable characteristics such as robustness to noise and simplicity to apply. However, the BEECLUST method does not perform well in low robot densities. In this article, inspired by the navigation techniques used by ants and bees, a self-adaptive landmark-based aggregation method is proposed. In this method, robots use landmarks in the environment to locate the cue once they "learn" the relative position of the cue with respect to the landmark. With the introduction of an error threshold parameter, the method also becomes adaptive to changes in the environment. Through systematic experiments in kinematic and realistic simulators with different parameters, robot densities, and cue sizes, it was observed that using the information of the environment makes the proposed method to show better performance than the BEECLUST in all the settings, including low robot density, high noise, and dynamic conditions

Extended Artificial Pheromone System for Swarm Robotic Applications

This paper proposes an artificial pheromone communication system inspired by social insects. The proposed model is an extension of the previously developed pheromone communication system, COS-Phi. The new model increases COS-Phi flexibility by adding two new features, namely, diffusion and advection. The proposed system consists of an LCD flat screen that is placed horizontally, overhead digital camera to track mobile robots, which move on the screen, and a computer, which simulates the pheromone behaviour and visualises its spatial distribution on the LCD. To investigate the feasibility of the proposed pheromone system, real micro-robots, Colias, were deployed which mimicked insects' role in tracking the pheromone sources. The results showed that, unlike the COS-Phi, the proposed system can simulate the impact of environmental characteristics, such as temperature, atmospheric pressure or wind, on the spatio-temporal distribution of the pheromone. Thus, the system allows studying behaviours of pheromone-based robotic swarms in various real-world conditions