Field Of Swarm Robotics Research Articles

Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models.

This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group formation. The swarm robots are designed to adaptively reconfigure their movements in response to environmental changes, mimicking the flexibility and robustness of fish foraging patterns. The simulation results show that the proposed approach demonstrates improved cooperation, efficiency, and adaptability in various scenarios. The proposed approach shows significant strides in the field of swarm robotics by successfully implementing fish-inspired foraging strategies. The integration of neurodynamic models with swarm intelligence not only enhances the autonomous capabilities of individual robots, but also improves the collective efficiency of the swarm robots.

Special Issue on Design of Swarm Intelligence Through Interdisciplinary Approach

In biological and social systems, a “swarm” refers to a group of individual units that behave as a single intelligent entity. “Swarm behavior,” the collective result of the local interactions among the group members, exhibits what is called “swarm intelligence.” By identifying the design principles of such swarm intelligence, we may be able to create swarm robots that are highly adaptable, fault tolerant, and dimensionally flexible. An interdisciplinary approach, including disciplines ranging from technology to biology to the mathematical sciences, for example, is used to elucidate the design principles of swarm intelligence. We believe that such knowledge will lead to transformations in the field of swarm robotics. This special issue highlights 19 exciting papers, including 13 research papers, five review papers, and one letter. Some papers focus on understanding the mechanism of real swarm phenomena, while the other papers focus on designing intelligent swarm systems. The keywords of the papers are as follows. • Swarm intelligence • Interdisciplinary approach • Decentralized control • Swarm robot • Collective behavior We would like to express our gratitude to all authors and reviewers, and we hope that this special issue contributes to future research and development in swarm intelligence.

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments.

Advanced Applications of Swarm Robots in Astronomy, Specifically in Outer Space and on Celestial Objects

Swarm robots are simple physical robots that work collectively, coordinating and communicating amongst each other resulting in intelligence and computational power far more than the capacity of a single robot amongst them; to do complex, big, time-consuming, and sophisticated tasks. These are simple, strong, robust, less prone to damage, inexpensive, easily producible, and fast; therefore, they are perfect for astronomical uses, especially in outer space and on celestial objects. The main basic focus of our paper is the applications of swarm robotics in Astronomy, specifically in space. To cover all the applications and not leave anything behind, we have developed a hierarchical system, named by us as Pingakshak’s system, and have divided the applications in groups such that even if there are applications beyond our present time's technology, the system, can still be useful, and the system developed is also flexible, i.e., more groups and applications can be added later. We then also have reviewed eleven research papers, two theses, and one book to give an idea about the amount of research that is being carried out in this field. We then have identified all the grey areas in the result section, pointing towards all the applications with very little and incomplete research and informing other researchers about the incompleteness in the field of swarm robotics.

Special Issue on Bio-Logging and Robotics

In the studies done to date on the swarm behaviors of animals, many different observational techniques have been developed, indicating the importance of such detailed observations. The techniques of researchers aiming to capture the swarm behavior of animals, which is normally visually unobservable, have included attaching microsensors to honey bees or ants and data loggers (micro recorders) to birds or mammals. Such techniques, collectively known as “bio-logging,” can go far in clarifying why we feel animals that exhibit swarm behaviors seem to have a sort of collective intelligence, or “swarm intelligence.” Furthermore, studies on the swarm behaviors of animals may provide important clues to researchers in the field of swarm robotics. It is in this context that this special issue presents papers on bio-logging technologies, the collective behaviors of animals, and various advanced measurement technologies related to them. This special issue consists of one review article and 14 research papers. The subjects cover a wide range of areas, including control engineering, data science, and ecology. Thus, bio-logging is an interdisciplinary area that can expect to see much growth in the near future. The editors are confident that this issue will greatly contribute to further progress in the field of bio-logging.

Behavioral specialization emerges from the embodiment of a robotic swarm

This paper focuses on the effect of the embodiment of robots on collective behavior in robotic swarms. The research field of swarm robotics emphasizes the importance of the embodiment of robots; however, only a few studies have discussed how it influences the collective behavior of a robotic swarm. In this paper, a path-formation task is performed by robotic swarms in computer simulations with and without considering collisions among robots to discuss the effect of the robot embodiment. Additionally, the experiments were performed with varying the size of robots. The robot controllers were obtained by an evolutionary robotics approach. The results show that the robot collisions would affect not only the performance of the robotic swarm but also the emergent behavior to accomplish the task. The robot collisions seem to provide feedback on robotic swarms to emerge the division of labor among robots to manage congestion.

Agreement algorithm using the trial and error method at the macrolevel

The best-of-n problem (Valentini et al. in Front Robot AI 4(9):1–18, 2017) is one of the decision-making problems in which many robots (agents) select the best option among a set of n alternatives and are focused on the field of Swarm Robotics. Almost all of the previous studies focused on binary decision-making scenarios ( $$n = 2$$ ) and could not be applied without any change in the case of $$n> 2$$ . It is necessary to satisfy constraints on the number of robots N, or the time required for reaching the best option is abruptly increased. Therefore, it is required to construct a method that can deal with $$n> 2$$ . In this paper, we propose an algorithm (BRT model, bias and rising threshold model) in which the time and the possibility of reaching agreement are not dependent on the number of robots N even when $$n> 2$$ . By computer experiments, our claims are verified within the tested parameter ranges.

Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm

Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

HoverBots: Precise Locomotion Using Robots That Are Designed for Manufacturability

Scaling up robot swarms to collectives of hundreds or even thousands without sacrificing sensing, processing, and locomotion capabilities is a challenging problem. Low-cost robots are potentially scalable but the majority of existing systems have limited capabilities, and these limitations substantially constrain the type of experiments that could be performed by robotics researchers. As an alternative to increasing the quantity of robots by reducing their functionality, we have developed a new technology that delivers increased functionality at low-cost. In this paper we present a comprehensive literature review on the most commonly used locomotion strategies of swarm robotic systems. We introduce a new type of low-friction locomotion—active low-friction locomotion—and we show its first implementation in the HoverBot system. The HoverBot system consists of an air-levitation and magnet table, and a HoverBot agent. HoverBot agents are levitating circuit boards which we have equipped with an array of planar coils and a Hall-effect sensor. The HoverBot agent uses its coils to pull itself towards magnetic anchors that are embedded into a levitation table. These robots: use active low-friction locomotion; consist of only surface-mount components; circumvent actuator calibration; are capable of odometry by using a single Hall-effect sensor; and perform precise movement. We conducted three-hours of experimental evaluation of the HoverBot system in which we observed the system performing more than ten-thousand steps. We also demonstrate formation movement, random collision, and straight collisions with two robots. This study demonstrates that active low-friction locomotion is an alternative to wheeled and slip-stick locomotion in the field of swarm robotics.

Understanding the Limitations of Particle Swarm Algorithm for Dynamic Optimization Tasks

One of the most widely used biomimicry algorithms is the Particle Swarm Optimization (PSO). Since its introduction in 1995, it has caught the attention of both researchers and academicians as a way of solving various optimization problems, such as in the fields of engineering and medicine, to computer image processing and mission critical operations. PSO has been widely applied in the field of swarm robotics, however, the trend of creating a new variant PSO for each swarm robotic project is alarming. We investigate the basic properties of PSO algorithms relevant to the implementation of swarm robotics and characterize the limitations that promote this trend to manifest. Experiments were conducted to investigate the convergence properties of three PSO variants (original PSO, SPSO and APSO) and the global optimum and local optimal of these PSO algorithms were determined. We were able to validate the existence of premature convergence in these PSO variants by comparing 16 functions implemented alongside the PSO variant. This highlighted the fundamental flaws in most variant PSOs, and signifies the importance of developing a more generalized PSO algorithm to support the implementation of swarm robotics. This is critical in curbing the influx of custom PSO and theoretically addresses the fundamental flaws of the existing PSO algorithm.

Modelling the swarm: Analysing biological and engineered swarm systems

In this article, we discuss the current research efforts on swarm systems and on the motivation of mathematical modelling of such distributed and self-organizing systems. Obviously, a special emphasis is given to those articles which were carefully selected for the special issue at hand. These articles demonstrate how mathematical models of different kinds – bottom-up agent models and top-down model approaches – increase the understanding of natural swarm systems, for example, by investigating the efficiency in the self-regulation of social insect colonies. A second group of articles illustrates how mathematical models are used to generate and optimize artificial swarm systems as engineered in the field of swarm robotics. We discuss the common problems of such modelling efforts and highlight the importance of models as generators of hypotheses that suggest novel empiric experiments as well as the importance of empiric experimentation that results in meaningful model parameterizations. In summary, the special issue at hand, which is introduced by this article, gives a significant overview about the lines of research that are followed in the research of swarm systems today.

Kalman Smoother Based Force Localization and Mapping Using Intravital Video Microscopy

Motility is an important property of immune system cells. It provides cells with the ability to perform their function not only at the right time but also at the right place. In this paper, we introduce the problem of modeling and estimating an effective force field directing cell movement by the analysis of intravital video microscopy. A computational approach is proposed for solving this problem without dealing with a parametrized spatial model of the field in order to avoid potential errors due to inaccurate spatial model assumptions. We consider the dynamics of cells similar to the dynamics of distributed agents typically used in the field of swarm robotics. The method utilizes a fixed-interval Kalman filter based smoother. Its application results in a map giving the intensity and direction of the effective force field. The results show that real-time video images are a source of data, enabling us to visualize intriguing spatiotemporal phenomena inside immune system organs. The proposed approach can fill the existing gap between contemporary technology and quantitative data analyses present in the field of biosystems.

Swarm Robots: From Self-assembly to Locomotion

Inspired by the swarm behaviours of social insects, research into the self-assembly of swarm robots has become an attractive issue in the robotic community. Unfortunately, there are very few platforms for self-assembly and locomotion in the field of swarm robotics. The Sambot is a novel self-assembling modular robot that shares characteristics with swarm robots and self-reconfigurable robots. Each Sambot can move autonomously and connect with the other. This paper discusses the concept of combining self-assembly and locomotion for swarm robots. Distributed control algorithms for self-assembly and locomotion are proposed. Using five physical Sambots, experiments were carried out on autonomous docking, self-assembly and locomotion. Our control algorithm for self-assembly can also be used to realize the autonomous construction and self-repair of robotic structures consisting of a large number of Sambots.