Virtual Pheromone Research Articles

Robustness and Scalability of Incomplete Virtual Pheromone Maps for Stigmergic Collective Exploration

The Swarm Guiding and Communication System (SGCS) is a decision-making and information-sharing framework for robot swarms that only needs close-range peer-to-peer communication and no centralized control. Each robot makes decisions based on an incomplete virtual pheromone map that is updated on each interaction with another robot, imitating ant colonial behavior. Similar systems rely on continuous communication with no range limitations, environment modification, or centralized control. A computer simulation is developed to assess the effectiveness and robustness of the framework in covering an area. Consistency and the time needed for 99% coverage are compared with an unbiased random walk. The pheromone approach is shown to outperfom the unbiased one regardless of number of agents. Innate resilience to individual failures is also demonstrated.

Research on Search Algorithm Using Particle Swarm Optimization with Virtual Pheromone for Swarm Robots

Research on Search Algorithm Using Particle Swarm Optimization with Virtual Pheromone for Swarm Robots

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

Autonomous vehicles have been highlighted as a major growth area for future transportation systems and the deployment of large numbers of these vehicles is expected when safety and legal challenges are overcome. To meet the necessary safety standards, effective collision avoidance technologies are required to ensure that the number of accidents are kept to a minimum. As large numbers of autonomous vehicles, operating together on roads, can be regarded as a swarm system, we propose a bio-inspired collision avoidance strategy using virtual pheromones; an approach that has evolved effectively in nature over many millions of years. Previous research using virtual pheromones showed the potential of pheromone-based systems to maneuver a swarm of robots. However, designing an individual controller to maximise the performance of the entire swarm is a major challenge. In this paper, we propose a novel deep reinforcement learning (DRL) based approach that is able to train a controller that introduces collision avoidance behaviour. To accelerate training, we propose a novel sampling strategy called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Highlight Experience Replay</i> and integrate it with a Deep Deterministic Policy Gradient algorithm with noise added to the weights and biases of the artificial neural network to improve exploration. To evaluate the performance of the proposed DRL-based controller, we applied it to navigation and collision avoidance tasks in three different traffic scenarios. The experimental results showed that the proposed DRL-based controller outperformed the manually-tuned controller in terms of stability, effectiveness, robustness and ease of tuning process. Furthermore, the proposed Highlight Experience Replay method outperformed than the popular Prioritized Experience Replay sampling strategy by taking 27% of training time average over three stages.

Research on Search Algorithm Using Particle Swarm Optimization with Virtual Pheromone for Swarm Robots

This paper proposes a search algorithm using particle swarm optimization (PSO) with virtual pheromone for swarm robots. Swarm robots are attracting attention in disaster relief works to search for victims. The search algorithm involves a combination of global and local searching. The conventional search method consists of random walk as the global search and PSO as the local search. However, random walk is not efficient in complex environments. For efficient searching, PSO with virtual pheromone is used for the global search. The virtual pheromone drives the swarm robots to an unsearched area, dose not need map data, and has low calculation cost. In addition, it is not necessary in the proposed method to switch algorithms between global and local searching. The validity of the proposed method was confirmed from the simulation results.

A Multiple Pheromone Communication System for Swarm Intelligence

Pheromones are chemical substances essential for communication among social insects. In the application of swarm intelligence to real micro mobile robots, the deployment of a single virtual pheromone has emerged recently as a powerful real-time method for indirect communication. However, these studies usually exploit only one kind of pheromones in their task, neglecting the crucial fact that in the world of real insects, multiple pheromones play important roles in shaping stigmergic behaviors such as foraging or nest building. To explore the multiple pheromones mechanism which enable robots to solve complex collective tasks efficiently, we introduce an artificial multiple pheromone system (ColCOS $\Phi $ ) to support swarm intelligence research by enabling multiple robots to deploy and react to multiple pheromones simultaneously. The proposed system ColCOS $\Phi $ uses optical signals to emulate different evaporating chemical substances i.e. pheromones. These emulated pheromones are represented by trails displayed on a wide LCD display screen positioned horizontally, on which multiple miniature robots can move freely. The color sensors beneath the robots can detect and identify lingering “pheromones” on the screen. Meanwhile, the release of any pheromone from each robot is enabled by monitoring its positional information over time with an overhead camera. No other communication methods apart from virtual pheromones are employed in this system. Two case studies have been carried out which have verified the feasibility and effectiveness of the proposed system in achieving complex swarm tasks as empowered by multiple pheromones. This novel platform is a timely and powerful tool for research into swarm intelligence.

Swarm Foraging Review: Closing the Gap Between Proof and Practice

We review recent research on swarm robot foraging and contextualize it with foundational work. Recent work can be divided into two complementary camps: self-organizing algorithms that provide practical gains and analytical research focus on theoretical proofs. Encouragingly, the convergence between theory and practice is evident in analytical work on the scaling of transportation networks and in behavioral grammars that give formal insight into emergent properties of foraging. Augmented reality has enabled virtual pheromones to be used with hardware, blurring the line between physical and simulation experiments. In this review we highlight bio-inspired and self-organizing approaches to swarm foraging and contrast them with approaches that can provide theoretical proofs, but which abstract away important features from foraging in real-world environments.

Virtual Pheromone Based Network Flow Control For Modular Robotic Systems

Guaranteeing data transmission between modules is the key for application development of modular robotic systems. In a multi-channel modular robotic system, intersection modules play an essential role of communication channel selection in controlling data flow toward desired destinations. The gradient-based routing algorithm is an ideal solution to create an one-way communication path from any robotic module to a designated destination. To create bi-directional communication for a communication path of robotic configuration, virtual pheromone-based routing algorithm is a promising mechanism for intersection modules due to its simplicity and distributivity. In this paper, we address a virtual pheromone based network flow control based on the integration of gradient and virtual pheromone-based routing algorithms. We validated this method through an education and entertainment application using our newly developed modular robotic system.

A novel foraging algorithm for swarm robotics based on virtual pheromones and neural network

Swarm robotics is an emerging interdisciplinary field that has many potential real-world applications. Swarm robotics aims to produce robust, scalable, and flexible self-organizing behaviors through local interactions from a large number of simple robots. In this paper, a novel pheromone model of swarm foraging behavior is developed based on a neural network. The output of a single neuron corresponds to the density of a pheromone, which diffuses to neighboring neurons through their local connections. A neural network is updated based on the proposed evaporation model. Neural networks can often mimic the dynamics and features of pheromones. Therefore, in this work, we develop an optimization method to determine the key parameters of cooperative foraging based on mathematical modeling. The differential equation variables represent the number of foraging robots assigned different tasks. The solutions of the differential equations represent the dynamics of the foraging behavior. The key parameters that affect task allocation are determined to make optimal decision rules. Simulation experiments are conducted under different foraging scenarios. The experimental results demonstrate the effectiveness of the proposed pheromone model.

Sophisticated collective foraging with minimalist agents: a swarm robotics test

How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.

A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots.

The advent of the swarm makes it feasible to dynamically monitor a wide area for maritime applications. The crucial problems of underwater swarm monitoring are communication and behavior coordination. To tackle these problems, we propose a wide area monitoring strategy that searches for static targets of interest simultaneously. Traditionally, an underwater robot adopts either acoustic communication or optical communication. However, the former is low in bandwidth and the latter is short in communication range. Our strategy coordinates underwater robots through indirect communication, which is inspired by social insects that exchange information by pheromone. The indirect communication is established with the help of a set of underwater communication nodes. We adopt a virtual pheromone-based controller and provide a set of rules to integrate the area of interest into the pheromone. Based on the information in the virtual pheromone, behavior laws are developed to guide the swarm to monitor and search with nearby information. In addition, a robot can improve its performance when using additional far-away pheromone information. The monitoring strategy is further improved by adopting a swarm evolution scheme which automatically adjusts the visiting period. Experimental results show that our strategy is superior to the random strategy in most cases.

Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors

In Turkey the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing a real-world Call Details Records (CDRs) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatiotemporal patterns: Computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample to a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allows computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: (i) correlated with the amount of interaction with locals; (ii) an effective proxy for refugee's economic capacity, thus refugee's potential employment; and (iii) able to capture events that may disrupt the integration phenomena, such as social tensions.

Unobtrusive Monitoring the Daily Activity Routine of Elderly People Living Alone, with Low-Cost Binary Sensors.

Most expert projections indicate that in 2030, there will be over one billion people aged 60 or over. The vast majority of them prefer to spend their last years at home, and almost a third of them live alone. This creates a growing need for technology-based solutions capable of helping older people to live independently in their places. Despite the wealth of solutions proposed for this general problem, there are very few support systems that can be reproduced on a larger scale. In this study, we propose a method to monitor the activity of the elderly living alone and detect deviations from the previous activity patterns based on the idea that the residential living environment can be modeled as a collection of behaviorally significant places located arbitrarily in a generic space. Then we use virtual pheromones—a concept defined in our previous work—to create images of the pheromone distribution maps, which describe the spatiotemporal evolution of the interactions between the user and the environment. We propose a method to detect deviations from the activity routines based on a simple statistical analysis of the resulting images. By applying this method on two public activity recognition datasets, we found that the system is capable of detecting both singular deviations and slow-deviating trends from the previous activity routine of the monitored persons.

A Family of ACO Routing Protocols for Mobile Ad Hoc Networks.

In this work, an ACO routing protocol for mobile ad hoc networks based on AntHocNet is specified. As its predecessor, this new protocol, called AntOR, is hybrid in the sense that it contains elements from both reactive and proactive routing. Specifically, it combines a reactive route setup process with a proactive route maintenance and improvement process. Key aspects of the AntOR protocol are the disjoint-link and disjoint-node routes, separation between the regular pheromone and the virtual pheromone in the diffusion process and the exploration of routes, taking into consideration the number of hops in the best routes. In this work, a family of ACO routing protocols based on AntOR is also specified. These protocols are based on protocol successive refinements. In this work, we also present a parallelized version of AntOR that we call PAntOR. Using programming multiprocessor architectures based on the shared memory protocol, PAntOR allows running tasks in parallel using threads. This parallelization is applicable in the route setup phase, route local repair process and link failure notification. In addition, a variant of PAntOR that consists of having more than one interface, which we call PAntOR-MI (PAntOR-Multiple Interface), is specified. This approach parallelizes the sending of broadcast messages by interface through threads.

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic

In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipėda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

Avoiding blind leading the blind

Virtual pheromone trailing has successfully been demonstrated for navigation of multiple robots to achieve a collective goal. Many previous works use a pheromone deposition scheme that assumes perfect localization of the robot, in which, robots precisely know their location in the map. Therefore, pheromones are always assumed to be deposited at the desired place. However, it is difficult to achieve perfect localization of the robot due to errors in encoders and sensors attached to the robot and the dynamics of the environment in which the robot operates. In real-world scenarios, there is always some uncertainty associated in estimating the pose (i.e. position and orientation) of the mobile service robot. Failing to model this uncertainty would result in service robots depositing pheromones at wrong places. A leading robot in the multi-robot system might completely fail to localize itself in the environment and be lost. Other robots trailing its pheromones will end up being in entirely wrong areas of the map. This results in a “blind leading the blind” scenario that reduces the efficiency of the multi-robot system. We propose a pheromone deposition algorithm, which models the uncertainty of the robot’s pose. We demonstrate, through experiments in both simulated and real environments, that modeling the uncertainty in pheromone deposition is crucial, and that the proposed algorithm can model the uncertainty well.

Virtual pheromone map building and a utilization method for a multi-purpose swarm robot system

Swarm robotics is an approach to realizing an intelligent system using swarm intelligence that emerges from complex interactions among individual robots. To implement a swarm robot system requires an efficient method for sharing and utilizing the information that is gathered by multiple robots. In this paper, we propose an algorithm to gather, simplify, share, and use the information in a multi-purpose swarm robot system. To minimize communications traffic among the robots, we use a graph-based map-building method. Furthermore, the nodes on the graph include information about the environment for control and interaction among the robots. We refer to this graph-based map as a virtual pheromone map. We propose an efficient algorithm to build a virtual pheromone map with low communications traffic and without contradictions using error-included sensors. The utilization method of the virtual pheromone map is efficient for implementing the interactions among the robots. The proposed algorithms are validated by a computer simulation. The result of the simulation shows that the virtual pheromone map-based swarm robot system works efficiently from the effects of swarm intelligence.

Engineering Human Stigmergy

Discovered in the context of a research about insects, stigmergy — the indirect coordination mechanism that allows ant colonies to achieve intelligent behavior — has been extensively studied with the aim to create artificial, ant-like agents. Although stigmergic behavior has been also identified in human collectivities, there are relatively few reports about technological solutions that facilitate the emergence of such interactions between people.This paper proposes the concept of virtual pheromones, defined as engrams created by the agents not in the environment, but in a representation thereof — a map, and outlines several use cases, wherein pheromones embedded in maps are the key element for inducing stigmergic behavior in human multi-agent systems.Without proposing a theoretic generalization, this paper aims to emphasize the broad range of possible technological applications of human stigmergy, and, maybe, to mark a new starting point for a more in-depth study of this topic.

A hybrid multi-path routing algorithm for industrial wireless mesh networks

Abstract Multi-path routing, a routing technique that enables data transmission over multiple paths, is an effective strategy in achieving reliability in wireless sensor networks. However, multi-path routing does not guarantee deterministic transmission. This is because more than one path is available for transferring data from the source node to the destination node. A hybrid multi-path routing algorithm is proposed for industrial wireless mesh networks for improving reliability and determinacy of data transmission, as well as to effectively deal with link failures. The proposed algorithm adopts the enhanced Dijkstra’s algorithm for searching the shortest route from the gateway to each end node for first route setup. A virtual pheromone distinct from the regular pheromone is introduced to realize pheromone diffusion and updating. In this way, multiple routes are searched based on the ant colony optimization algorithm. The routes used for data transmission are selected based on their regular pheromone values, facilitating the delivery of data through better routes. Link failures are then handled using route maintenance mechanism. Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of average end-to-end delay, packet delivery ratio, and routing overhead; moreover, it has a strong capacity to cope with topological changes, thereby making it more suitable for industrial wireless mesh networks.

Robot Coordination Based on Biologically Inspired Methods

An embedded particle swarm optimization (PSO) technique combined with virtual pheromones deposition and rules for artificial bird flocking is proposed to handle an area coverage problem using a swarm of mobile robots. A simulation tool VERA that was developed to simulate a swarm behavior of a group of mobile agents is described. Results of simulation experiments and tests on Lego robots that prove the concept are presented. Results are discussed and future development is suggested in the end of the paper.

Adapting an Ant Colony Metaphor for Multi-Robot Chemical Plume Tracing

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments.