A Multiple Pheromone Communication System for Swarm Intelligence

Liu, Tian; Sun, Xuelong; Hu, Cheng; Fu, Qinbing; Yue, Shigang

doi:10.1109/access.2021.3124386

Cited by 9 publications

(11 citation statements)

References 48 publications

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“…To realize the prey-predator interaction scenario delineated in this study within the physical realm, a robotics platform equipped with odor field rendering, localization, and visual sensing capabilities is essential. Among available swarm robotics platforms, the recently developed VColCOSΦ platform [25,26] aligns better with these requirements. This platform was meticulously designed for conducting swarm robotics research, equipped with the capability to construct and detect multi-modal information.…”

Section: Real Robot Implementationmentioning

confidence: 99%

“…The recent development of agent-based simulation [22,23] and the robotics technology [24][25][26][27][28][29] provides an opportunity to devise a system that could conduct real-time and real-world experiments of prey-predator interaction scenarios with multi-modal sensory and evolutionary processing involved. We highlight that bringing the multi-modal preypredator interaction studies from virtual computer to real physical world matters at least in the following aspects: (1) Robots, designed to navigate complex environments, offer a more precise depiction of interactions than any other simulations [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 4. The swarm robotics platform VColCOSΦ platform[25,26] employed for conducting the prey-predator interaction scenario defined in this study.…”

mentioning

confidence: 99%

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Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics

Sun,

Hu,

Liu

et al. 2023

Biomimetics

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Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints.

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Section: Real Robot Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics

Sun,

Hu,

Liu

et al. 2023

Biomimetics

Self Cite

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“…Pheromone-based methods mainly rely on the use of multiple types of virtual pheromones, which can be either repulsive or attractive according to the task(s) of the swarm (e.g., repulsive for area coverage, attractive for target tracking). Liu, Sun, Hu, Fu, and Yue (2021) investigate the performance of a model using two types of pheromones in the context of the deployment of a robot swarm. They use for that purpose a pheromone system, named ColCOSΦ(Colour-based COmmunication System of pheromone), designed by Sun, Liu, Hu, Fu, and Yue (2019).…”

Section: Pheromone Systemmentioning

confidence: 99%

Learning to Optimise a Swarm of UAVs

Duflo

Danoy

Talbi

et al. 2022

Preprint

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The usage of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase of interest in the past few years. UAVs find applications where human action would be ineffective, slow, risky or even impossible. With a three-dimensional mobility and payload flexibility, they can indeed be used for missions like infrastructure inspection or search and rescue. Most applications have considered the usage of a single UAV so far, but using several autonomous UAVs as a swarm would overcome some drawbacks like mission duration (if one UAV of the swarm is out of battery, the latter can pause the mission while others keep flying) or payload capacity (the payload can be distributed among all UAVs). Designing an efficient swarm of UAVs however comes with some challenges, including the difficulty to define the necessary distributed algorithms to tackle specific tasks. The desired global behaviour, e.g. monitoring an area or transporting material, is indeed emergent from local interactions. Manually designing these local interactions can therefore be tedious and time-consuming. This work thus aims at automating that process in the context of area coverage. The first step has been to define a multi-objective optimisation problem to represent an area coverage mission. The second step has been to define an algorithm to generate distributed heuristic for the latter optimisation problem. The proposed method is based on Q-learning and experimental results demonstrate that it permits to generate heuristics that not only outperform the state-of-the-art, but also provide a high stability.

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“…According to the driving sources, the swarm of micro/nanorobots can currently be generated by optical field, [48,49] magnetic field, [50,51] ultrasonic field, [52] and chemical reactions. [53] Among them, a magnetically driven strategy offers significant advantages. For instance, the magnetic field can wirelessly manipulate micro/nanorobots in a noninvasive manner, without the need for toxic fuels required in chemical driving or high-intensity light sources needed in optical driving.…”

Section: Introductionmentioning

confidence: 99%

Design and Control of the Magnetically Actuated Micro/Nanorobot Swarm toward Biomedical Applications

Lu,

Zhao,

et al. 2024

Adv Healthcare Materials

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Recently, magnetically actuated micro/nanorobots hold extensive promises in biomedical applications due to their advantages of non‐invasiveness, fuel‐free operation, and programmable nature. While effectively promised in various fields such as targeted delivery, most past investigations are mainly displayed in magnetic control of individual micro/nanorobots. Facing practical medical use, the micro/nanorobots are required for the development of swarm control in a closed‐loop control manner. This review outlines the recent developments in magnetic micro/nanorobot swarms, including their actuating fundamentals, designs, controls, and biomedical applications. The fundamental principles and interactions involved in the formation of magnetic micro/nanorobot swarms are discussed first. The recent advances in the design of artificial and biohybrid micro/nanorobot swarms, along with the control devices and methods used for swarm manipulation, are presented. Furthermore, biomedical applications that have the potential to achieve clinical application are introduced, such as imaging‐guided therapy, targeted delivery, embolization, and biofilm eradication. By addressing the potential challenges discussed towards the end of this review, magnetic micro/nanorobot swarms hold promise for clinical treatments in the future.This article is protected by copyright. All rights reserved

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A Multiple Pheromone Communication System for Swarm Intelligence

Cited by 9 publications

References 48 publications

Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics

Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics

Learning to Optimise a Swarm of UAVs

Design and Control of the Magnetically Actuated Micro/Nanorobot Swarm toward Biomedical Applications

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