Firefly-Inspired Synchronization in Swarms of Mobile Agents

Synchronization can be a necessary prerequisite to perform coordinated actions or reach consensus in decentralized multi-agent systems, such as robotic swarms and sensor networks. One of the simplest distributed synchronization algorithms is firefly synchronization, also known as pulse-coupled oscillator synchronization. In this framework, each agent possesses an internal oscillator and the completion of oscillation cycles is signaled by means of short pulses, which can be detected by other neighboring agents. This thesis focuses on a realistic mode of interaction for practical implementations, in which agents have a restricted field of view used to detect pulses emitted by other agents. The effect of agent speed on the time required to achieve synchronization is studied. Simulations reveal that synchronization can be fostered or inhibited by tuning the agent (robot) speed, leading to distinct dynamical regimes. These findings are further validated in physical robotic experiments. In addition, an analysis is presented on the effect that the involved system parameters have on the time it takes for the ensemble to synchronize. To assess the effect of noise, the propagation of perturbations over the system is analyzed. The reported findings reveal the conditions for the control of clock or activity synchronization in swarms of mobile agents

Firefly-Inspired Synchronization in Swarms of Mobile Agents

Recently, there has been growing interest in the synchronization of mobile pulse-coupled oscillators. We build on the work by Prignano et al. (Phys. Rev. Lett. 110, 114101) and show that agents that interact exclusively with others in their cone of vision can exhibit different synchronization regimes. Depending on their speed, synchronization emerges as a slow process through spreading of the local coherence, as a fast process where global synchronization dominates, or it is inhibited for a range of intermediate speeds. In addition, we show that, not only the speed of the agents, but also their angle and range of interaction can tune the appearance of this intermediate regime

Concepts and evolution of research in the field of wireless sensor networks

The field of Wireless Sensor Networks (WSNs) is experiencing a resurgence of interest and a continuous evolution in the scientific and industrial community. The use of this particular type of ad hoc network is becoming increasingly important in many contexts, regardless of geographical position and so, according to a set of possible application. WSNs offer interesting low cost and easily deployable solutions to perform a remote real time monitoring, target tracking and recognition of physical phenomenon. The uses of these sensors organized into a network continue to reveal a set of research questions according to particularities target applications. Despite difficulties introduced by sensor resources constraints, research contributions in this field are growing day by day. In this paper, we present a comprehensive review of most recent literature of WSNs and outline open research issues in this field

Impact of step size on convergence in swarmalator systems

openGroup robotics is one of the key areas of the development of robotic systems. This is due to the fact that for a wide class of practical tasks, the use of a group of relatively simple robots is much more efficient than using a single large multi-purpose device. The modern development of computer technology and communication systems opens up wide opportunities for the construction of such systems. The most progressive and effective approach is the implementation of the collective behavior of robots according to the swarm principle, when each of them interacts only with neighboring individuals, synchronously exchanging the collected information about the environment and their condition. Such a group compensates for the weakness of its detection and communication devices by joining a team. The problems of introducing group robotics into the modern world are studied in this thesis. If they combine two concepts, synchronization and swarming, they are called a swarmalator. In swarmalator systems, the movement of the robots is governed by differential equations. These equations are solved with the Euler method, where the location and phase are determined. The Euler method is time-discrete and allows the integration of first-order differential equations. Therefore, there is a step size to be chosen. The main task is to study group movement, which is based on transmitting information with a definite step size. The step value affects how often the swarmalators share their location and phase. Three main conclusions are made. The first research is what happens when varying the step size - is it most optimal to use with small step sizes? The second conclusion is that when increasing the step size with a small increment or using randomization of the step size. Such methods are typically, more optimal to use with a gradual increase in the step size because the convergence time is lower. The third is when decreasing the step size using a small increment. The results showed that this method is optimal to use when the step size exceeds 1. The states converge at a rather large interval, compared with previous results, but at the same time with a large value of the convergence time. The values of optimal step sizes are presented and analyzed. As performance criteria, we consider the computational power that is required, the average convergence time, the coupling probability and the step size. The behavior of all parameters is graphically represented in plots. The conclusions are based on the simulations done for the results.Group robotics is one of the key areas of the development of robotic systems. This is due to the fact that for a wide class of practical tasks, the use of a group of relatively simple robots is much more efficient than using a single large multi-purpose device. The modern development of computer technology and communication systems opens up wide opportunities for the construction of such systems. The most progressive and effective approach is the implementation of the collective behavior of robots according to the swarm principle, when each of them interacts only with neighboring individuals, synchronously exchanging the collected information about the environment and their condition. Such a group compensates for the weakness of its detection and communication devices by joining a team. The problems of introducing group robotics into the modern world are studied in this thesis. If they combine two concepts, synchronization and swarming, they are called a swarmalator. In swarmalator systems, the movement of the robots is governed by differential equations. These equations are solved with the Euler method, where the location and phase are determined. The Euler method is time-discrete and allows the integration of first-order differential equations. Therefore, there is a step size to be chosen. The main task is to study group movement, which is based on transmitting information with a definite step size. The step value affects how often the swarmalators share their location and phase. Three main conclusions are made. The first research is what happens when varying the step size - is it most optimal to use with small step sizes? The second conclusion is that when increasing the step size with a small increment or using randomization of the step size. Such methods are typically, more optimal to use with a gradual increase in the step size because the convergence time is lower. The third is when decreasing the step size using a small increment. The results showed that this method is optimal to use when the step size exceeds 1. The states converge at a rather large interval, compared with previous results, but at the same time with a large value of the convergence time. The values of optimal step sizes are presented and analyzed. As performance criteria, we consider the computational power that is required, the average convergence time, the coupling probability and the step size. The behavior of all parameters is graphically represented in plots. The conclusions are based on the simulations done for the results

A survey of modern exogenous fault detection and diagnosis methods for swarm robotics

Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robust-ness being viewed as an inherent property of them. However, this has been shown to not always be thecase. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importancefor ensuring the continued operation and success of the swarm. This paper provides an overview of recentwork in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areaswhere research is concentrated: immune system, data modelling, and blockchain-based fault detectionmethods and local-sensing based fault diagnosis methods. Each of these areas have significant advan-tages and disadvantages which are explored in detail. Though the work presented here represents a sig-nificant advancement in the field, there are still large areas that require further research. Specifically,further research is required in testing these methods on real robotic swarms, fault diagnosis methods,and integrating fault detection, diagnosis and recovery methods in order to create robust swarms thatcan be used for non-trivial tasks

Motion Planning of UAV Swarm: Recent Challenges and Approaches

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work