In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.

Swarm Robotic Behaviors and Current Applications

We observed the impact of bad pollen supply (non-foraging due to artificial rain and pollen removal under poor-foraging conditions) on the survival of honey bee larvae, and on the total development time from egg-laying to the capping of a larval cell. Five days of non-foraging led to cannibalism of larvae younger than 3 days old and to a shortening of the time until larvae were sealed, but 4- and 5-day-old larvae survived even worse pollen supply situations. Manual pollen removal and reduction of income (pollen trap) induced cannibalism of younger larvae. The larvae's mean capping age significantly correlated with the mean pollen income: the less pollen was stored by the hive during the larvae's development, the earlier the larvae were capped. Both behavioral patterns lead to a quick reduction in the amount of unsealed older brood in response to a shortage of available protein. Older larvae have the highest pollen demand, so this strategy compensates for a shortage of supply by reducing demand. Additionally worker jelly gets enriched by protein gained from cannibalism, and the early capping of older larvae saves the oldest part of the brood, which represents the highest broodcare investment.

Cannibalism and early capping: strategy of honeybee colonies in times of experimental pollen shortages A Neuroethology, sensory, neural, and behavioral physiology

The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

In smart environments, traditional information management approaches are often unsuitable to tackle with the needed elaborations due to the amount and the high dynamicity of entities involved. Smart objects (enhanced devices or IoT services belonging to a smart system) interact and maintain relations which need of effective and efficient selection/filtering mechanisms to better meet users’ requirements. Recommender systems provide useful and customized information, properly selected and filtered, for users and services. This paper proposes a heuristic method to build a recommender engine in IoT environment exploiting swarm intelligence techniques. Smart objects are represented using real-valued vectors obtained through the Doc2Vec model, a word embedding technique able to capture the semantic context representing documents and sentences with dense vectors. The vectors are associated to mobile agents that move in a virtual 2D space following a bio-inspired model - the flocking model - in which agents perform simple and local operations autonomously obtaining a global intelligent organization. A similarity rule, based on the assigned vectors, was designed so enabling agents to discriminate among them. A closer positioning (clustering) of only similar agents is achieved. The intelligent positioning allows easy identifying of similar smart objects, thus enabling a fast and effective selection operations. Experimental evaluations have allowed to demonstrate the validity of the approach, and on how the proposed methodology allows obtaining an increasing in performance of about 50%, in terms of clustering quality and relevance, compared to other existing approaches.

Heuristic recommendation technique in Internet of Things featuring swarm intelligence approach

Cyber-physical systems research and education in 2030: Scenarios and strategies

Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system designed after swarm intelligence, each agent acts autonomously, reacts on dynamic inputs, and, implicitly or explicitly, works collaboratively with other swarm members without a central control. The system as a whole is expected to exhibit global patterns and behaviors. Although well-designed swarms can show advantages in adaptability, robustness, and scalability, it must be noted that SI system have not really found their way from lab demonstrations to real-world applications, so far. This is particularly true for embodied SI, where the agents are physical entities, such as in swarm robotics scenarios. In this paper, we start from these observations, outline different definitions and characterizations, and then discuss present challenges in the perspective of future use of swarm intelligence. These include application ideas, research topics, and new sources of inspiration from biology, physics, and human cognition. To motivate future applications of swarms, we make use of the notion of cyber-physical systems (CPS). CPSs are a way to encompass the large spectrum of technologies including robotics, internet of things (IoT), Systems on Chip (SoC), embedded systems, and so on. Thereby, we give concrete examples for visionary applications and their challenges representing the physical embodiment of swarm intelligence in autonomous driving and smart traffic, emergency response, environmental monitoring, electric energy grids, space missions, medical applications, and human networks. We do not aim to provide new solutions for the swarm intelligence or CPS community, but rather build a bridge between these two communities. This allows us to view the research problems of swarm intelligence from a broader perspective and motivate future research activities in modeling, design, validation/verification, and human-in-the-loop concepts.

https://www.research.manchester.ac.uk/portal/files/175341536/1_s2.0_S2210650220304156_main.pdf

Ahmet Şekercioğlu

Papers

Swarm Intelligence and Cyber-Physical Systems: Concepts, Challenges and Future Trends