Showing papers on "Ant robotics published in 2007"

Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization

Abstract: Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Techniques have been adopted for multi-robot search from the particle swarm optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by particle swarm optimization. Additionally, we exploit this inspiration by modifying the particle swarm optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range

Towards Energy Optimization: Emergent Task Allocation in a Swarm of Foraging Robots

Abstract: This article presents a simple adaptation mechanism to automatically adjust the ratio of foragers to resters (division of labor) in a swarm of foraging robots and hence maximize the net energy income to the swarm. Three adaptation rules are introduced based on local sensing and communications. Individual robots use internal cues (successful food retrieval), environmental cues (collisions with team-mates while searching for food) and social cues (team-mate success in food retrieval) to dynamically vary the time spent foraging or resting. Simulation results show that the swarm demonstrates successful adaptive emergent division of labor and robustness to environmental change (in food source density), and we observe that robots need to cooperate more when food is scarce. Furthermore, the adaptation mechanism is able to guide the swarm towards energy optimization despite the limited sensing and communication abilities of the individual robots and the simple social interaction rules. The swarm also exhibits the capacity to collectively perceive environmental changes; a capacity that can only be observed at a group level and cannot be deduced from individual robots.

Alice in Pheromone Land: An Experimental Setup for the Study of Ant-like Robots

Abstract: The pheromone trail laying and trail following behaviors of ants have proved to be an efficient mechanism to optimize path selection in natural as well as in artificial networks. Despite this efficiency, this mechanism is under-used in collective robotics because of the chemical nature of pheromones. In this paper we present a new experimental setup which allows to investigate with real robots the properties of a robotics systems using such behaviors. To validate our setup, we present the results of an experiment in which a group of 5 robots has to select between two identical alternatives a path linking two different areas. Moreover, a set of computer simulations provides a more complete exploration of the properties of this system. At last, experimental and simulation results lead us to interesting prediction that will be testable in our setup.

Bio-Inspired Group Behaviors for the Deployment of a Swarm of Robots to Multiple Destinations

Abstract: We present a methodology for characterizing and synthesizing swarm behaviors using both a macroscopic model that represents a swarm as a continuum and a microscopic model that represents individual robots. We develop a systematic approach for synthesizing behaviors at the macroscopic level that can be realized on individual robots at the microscopic level. Our methodology is inspired by a dynamical model of ant house hunting [1], a decentralized process in which a colony attempts to emigrate to the best site among several alternatives. The model is hybrid because the colony switches between different sets of behaviors, or modes, during this process. At the macroscopic level, we are able to synthesize controllers that result in the deployment of a robotic swarm in a predefined ratio between distinct sites. We then derive hybrid controllers for individual robots using only local interactions and no communication that respect the specifications of the global continuous behavior. Our simulations demonstrate that our synthesis procedure yields a correct microscopic model from the macroscopic description with guarantees on performance at both levels

Swarm intelligence and its applications in swarm robotics

Abstract: This work gives an overview of the broad field of computational swarm intelligence and its applications in swarm robotics. Computational swarm intelligence is modelled on the social behavior of animals and its principle application is as an optimization technique. Swarm robotics is a relatively new and rapidly developing field which draws inspiration from swarm intelligence. It is an interesting alternative to classical approaches to robotics because of some properties of problem solving present in social insects, which is flexible, robust, decentralized and self-organized. This work highlights the possibilities for further research.

Swarm robotics and minimalism

Abstract: Swarm Robotics (SR) is closely related to Swarm Intelligence, and both were initially inspired by studies of social insects. Their guiding principles are based on their biological inspiration and take the form of an emphasis on decentralized local control and communication. Earlier studies went a step further in emphasizing the use of simple reactive robots that only communicate indirectly through the environment. More recently SR studies have moved beyond these constraints to explore the use of non-reactive robots that communicate directly, and that can learn and represent their environment. There is no clear agreement in the literature about how far such extensions of the original principles could go. Should there be any limitations on the individual abilities of the robots used in SR studies? Should knowledge of the capabilities of social insects lead to constraints on the capabilities of individual robots in SR studies? There is a lack of explicit discussion of such questions, and researchers have adopted a variety of constraints for a variety of reasons. A simple taxonomy of swarm robotics is presented here with the aim of addressing and clarifying these questions. The taxonomy distinguishes subareas of SR based on the emphases and justifications for minimalism and individual simplicity.

A Hybrid ACO/PSO Control Algorithm for Distributed Swarm Robots

Abstract: In this paper, we present a hybrid ant colony optimization/particle swarm optimization (ACO/PSO) control algorithm for distributed swarm robots, where each robot can only communicate with its neighbors within its communication range. A virtual pheromone mechanism is proposed as the message passing coordination scheme among the robots. This hybrid ACO/PSO architecture adopts the feedback mechanism from environment of ACO and the adaptive interplay among agents of PSO to create a dynamic optimization system, and it is well-suited for a large scale distributed multi-agent system under dynamic environments. Furthermore, a pheromone-edge pair propagation funneling method is developed to reduce the communication overhead among robots. The simulation results concretely demonstrate the robustness, scalability, and individual simplicity of the proposed control architecture in a swarm robot system with real-world constraints

From real robot swarm to evolutionary multi-robot organism

Abstract: Collective working allows microrobots to achieve more functionality, better performance and higher reliability on the macroscopic level. In this paper we demonstrate the on-going work in developing novel collective systems, where swarm robots work not only collectively, but are also capable of autonomous aggregation and disaggregation into a higher multi- robot organism. The main issues of such an organism, as well as its genome-based control, are discussed. We show the developed docking approach and investigate topological transformations in a prototype of self-assembling robots.

An Analysis of Emergent Taxis in a Wireless Connected Swarm of Mobile Robots

Abstract: In swarm robotic systems emergent swarm properties are particularly difficult to analyse and model. This paper describes a simple but effective algorithm for emergent swarm taxis (swarm motion toward a beacon) in a 2D or 3D wireless connected swarm of minimalist mobile robots. The paper then undertakes a deep analysis of the swarm taxis by identifying both first and second order micro-level robot interactions and quantifying the contribution of each such interaction to the macro-level swarm behaviour. From the analysis we develop a simple quantitative model that is able to predict swarm velocity with reasonable accuracy. Although the analysis is specific to the swarm algorithm in question, we believe that the methodology presented has generic value to swarm modellers.

Kobot: A mobile robot designed specifically for swarm robotics research

Abstract: The requirements of a mobile robot to be used as part of a swarm robotic system differs from that of a mobile robot to be used as stand-alone. In this paper, we first provide a wishlist of requirements that would be sought for in mobile robot platforms to be used in swarm robotics research. Then, we describe Kobot, as a new mobile robot platform which is designed to satisfy as much of these requirements. Specifically, we first describe in detail, an infrared-based short-range sensing system that can make proximity measurements with minimal interference from environmental lighting conditions as well as from other robots. The performance of the system, is evaluated with systematic experiments. Then, we present an IEEE802.15.4/ZigBee-based communication system, which is used to develop a system, that can wirelessly program robots either one-by-one or in parallel. Finally, we provide snapshots from the flocking of a Kobot robotic swarm. The paper, reviews and evaluates existing robot platforms that are developed for, or being used in, swarm robotics research in comparison with the Kobot platform and concludes.

Adaptive Flocking of a Swarm of Robots Based on Local Interactions

Abstract: This paper presents a novel flocking strategy for a large-scale swarm of robots that enables the robots to navigate autonomously in an environment populated with obstacles. Robot swarms are often required to move toward a goal while adapting to changes in environmental conditions in many applications. Based on the observation of the swimming behavior of a school of tunas, we apply their unique patterns of behavior to the autonomous adaptation of the shape of robot swarms. Specifically, each robot dynamically selects two neighboring robots within its sensing range and maintains a uniform distance with them. This enables three neighboring robots to form a regular triangle and remain stable in the presence of obstacles. Therefore, the swarm can be split into multiple groups or re-united into one according to environmental conditions. More specifically, assuming that robots are not allowed to have individual identification numbers, a pre-determined leader, memories of previous perceptions and actions, and direct communications to each other, we verify the validity of the proposed algorithm using the in-house simulator. The results show that a swarm of robots repeats the process of partition and maintenance passing through multiple narrow passageways

A novel ant clustering algorithm based on cellular automata

Abstract: Based on the cellular automata in artificial life, an artificial Ants Sleeping Model (ASM) and an ant algorithm for cluster analysis (A$^4$C) are presented. By simulating the swarm intelligence of the real ant colonies, we use the ant agent to represent the data object. In ASM, each ant has two states: sleeping state and active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants to become active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and consequently the data objects they represent are clustered. Experimental results show that the A$^4$C algorithm on ASM is significantly superior to other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient.

Evolving ant colony system for optimizing path planning in mobile robots

Abstract: Path planning is one of the problems in robotics. It consists on automatically determine a path from an initial position of the robot to its final position. In this paper we propose a variant of the ant colony system (ACO) applied to optimize the path that a robot can follow to reach its target destination. We also propose to evolve some parameters of the ACO algorithm by using a genetic algorithm (ACO-GA) to optimize the search of the shortest path. We compare the accuracy of ACO against ACO-GA using real environments.

Geometric formations in swarm aggregation: An artificial formation force based approach

Abstract: Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm) into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter-member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc.

Performance benefits of self-assembly in a swarm-bot

Abstract: Mobile robots are said to be capable of self- assembly when they can autonomously form physical connections with each other. Despite the recent proliferation of self- assembling systems, little work has been done on using self- assembly to add functional value to a robotic system, and even less on quantifying the contribution of self-assembly to system performance. In this study we demonstrate and quantify the performance benefits of i) acting as a physically larger self-assembled entity, ii) using self-assembly adaptively and iii) making the robots morphologically aware (the self-assembled robots leverage their new connected morphology in a task specific way). In our experiments, two real robots must navigate to a target over a-priori unknown terrain. In some cases the terrain can only be overcome by a self-assembled connected entity. In other cases, the robots can reach the target faster by navigating individually.

Swarm intelligence for cooperation of bio-nano robots using quorum sensing

Abstract: Bio-nano robots are nano-scaled robots made from biological components like proteins and DNA structures. Their nano-scaled size, ready availability (in nature), and high efficiency make them perfect tools for diagnosis and therapeutic treatments in nano-medicine. Due to their nano-scaled size, the intelligence of each individual nano robot is small when compared to that of the collection of nano robots acting together to accomplish the given task. This group intelligence, called swarm intelligence, helps the nano robots do their task more effectively, more quickly, and with fewer other resources. The coordination to accomplish the given task can be achieved by these nano robots through quorum sensing. Quorum sensing is the ability of nano robots to communicate and coordinate behavior via signaling molecules. The whole scenario of communication and coordination can be done using these nano-scaled robots and the results are studied using simulation at a high level of abstraction

Swarm robots aggregation formation control inspired by fish school

Abstract: Aggregation formation control strategy for swarm robots is proposed based on fish school model. Three behaviors: repulsive behavior, parallel behavior and attractive behavior, are introduced which make swarm robots form fish school like aggregation formation. Motor Schemas for avoiding obstacles and moving to target are designed which enable swarm robots to avoid obstacles and reach target. Individual robot autonomously adjusts its position based on its local sensing, so the aggregation formation control strategy is flexible and robust. Simulation experiments verify the validity of the control strategy.

Evolutionary Swarms of Flying Robots

Abstract: Keywords: Swarm Robotics ; Flying Robotics ; Evolutionary Robotics Reference LIS-CONF-2007-007 URL: http://fir.epfl.ch/ Record created on 2007-09-06, modified on 2016-08-08

Efficient multi-foraging in swarm robotics

Abstract: In the multi-foraging task studied in this paper, a group of robots has to efficiently retrieve two different types of prey to a nest. Robots have to decide when they leave the nest to forage and which prey to retrieve. The goal of this study is to identify an efficient multi-foraging behaviour, where efficiency is defined as a function of the energy that is spent by the robots during exploration and gained when a prey is retrieved to the nest. We design and validate a mathematical model that is used to predict the optimal behaviour. We introduce a decision algorithm and use simulations to study its performance in a wide range of experimental situations with respect to the predictions of the mathematical model.

Research progress of swarm intelligence

Abstract: Swarm Intelligence is the global intelligent behavior emerged from the interaction of groups of simple agents.From the view of complex systems,this paper comprehensively discusses the system structure,operation mechanism,modeling tools,algorithmic model and applications of swarm intelligence according to its fundamental principles.Firstly,around the system structure of swarm intelligence represented especially by ant colony and bird flock,the agents' attributes,their behavior rules and their interaction modes are analyzed,and then the feedback mechanisms and learning mechanisms implied in swarm intelligence are induced and revealed.Based on the introductions to the commonly-used modeling tools such as Genetic Algorithm(GA),Artificial Neural Network(ANN),Cellular Automata(CA),and Agent-Based Modeling(ABM),etc.,four kinds of typical models and algorithms of swarm intelligence,viz.,ant colony foraging,ant clustering, labor division in ant colony,and bird flock foraging,are discussed in detail, aiming to conclude the general rules to model and simulate the complex systems based on swarm intelligence.Finally,the applications of swarm intelligence to engineering optimization,production management,robotics,data analysis and pattern recognition are introduced,and some perspectives on the development of swarm intelligence are made as the concluding remarks of this paper.

Swarm Robot Systems Based on the Evolution of Personality Traits

Abstract: Game theory may be very useful in modeling and analyzing swarms of robots. Using game theory in conjunction with traits of personalities, we achieve intelligent swarm robots. Traits of personality are characteristics of each robot that define the robots’ behaviours. The environment is represented as a game and due to the evolution of the traits through a learning process, we show how the robots may react intelligently to changes in the environment. A proof of convergence for the proposed algorithm is offered. The process of selection of traits is discussed and the potential of the modeling is demonstrated in several different simulations.

Research on Multi-Robot System Inspired by Biological Swarm Intelligence

Abstract: Swarm intelligence in biology society is introduced firstly.Then such main research topics of collective robotics as collective control,swarm communication and swarm composition are discussed;the future development trends and several typical tasks for collective robots are introduced.Finally,the simulation on clustering task is given to show the application of swarm intelligence to multi-robot systems.

RobotMeme - A Proposal of Human-Robot Mimetic Mutual Adaptation-

Abstract: In late years, as new media turning into PC or mobile phone, a study about communication robots is prosperous. Robots are different from the conventional media, because robots have physical bodies like humans, so it is reported that humans bemust robots socially. Therefore, we decide to apply a concept of meme as a cultural gene to an interaction design with humans and robots in this study. By this design theory, we will realize Mimetic Mutual Adaptation by humans and robots imitating and adapting each other, exceeding a conventional form of oneway adaptation from humans to the media. Therefore, we called cultural information transmitted from robots "RobotMeme", we try that robots acquire cultural behaviors shared by human society and the robots transmits these meme to other robots by Human-Robot Mimetic Mutual Adaptation. Furthermore, we suggest "A Design of RobotMeme" to realize that robots create new cultural behaviors through human-robot interaction. In this paper, we describe an early stage of experiments to inspect whether RobotMeme were transmitted to human and observed that humans acquired original cultural behaviors of robots by imitation. From the results of these experiments, it is suggested that robots and human are going to be able to form the relations of interdependence by imitating each other.

Robots of the future

Abstract: Robots are a subset of “smart structures” — engineered constructs that can “think” and adapt to the environment. Robots can work even in environments not conducive to humans (e.g. areas with high levels of nuclear radiation). Intelligent robots will find a large range of applications in defence and nuclear industries, as also other scientific, technological, and commercial applications. Robotics is therefore a strategically important field of research. The question is: How smart or intelligent can the robots become? The answer is that perhaps there is no readily-conceivable upper limit. In fact, there is a strong section of opinion that, within the present century itself, robots will overtake humans in practically all aspects of mental and physical capability, and they will then evolve further at a rapid rate, with or without our help. Expert opinion is bound to be divided when we look too far into the future. Some scientists think that there is nothing much that we can do to control or prevent the inevitable and fast evolution of machine intelligence.

A fully decentralized approach for incremental perception

Abstract: The area of Swarm Robotics is still in its infancy. Key concepts at the basic level have to be invented and developed in order to achieve the future goal of building large scale physical and controllable autonomous robotic swarms. In this paper we extend the concept of Incremental Perception in swarm robotics into the domain of complete decentralization. Our work is aimed at micro-robotic swarms where the hardware resources available for the robots will be limited. Hence a decentralized system becomes inevitable because it does not require intra-dependence of robot agents, their monitoring system or a communication mechanism for the agents; absence of all these factors results in reduced hardware requirements for the agents. We focus on the co-operative behavior of robots rather than relying on their individual capabilities. We also propose the parameters and functions that are required for a completely decentralized system and show that such a system can be successfully modeled and analyzed.

Swarm potential fields with internal agent states and collective behaviour

Abstract: Swarm robotics is a new and promising approach to the design and control of multi-agent robotic systems. In this paper we use a model for a system of self-propelled agents interacting via pairwise attractive and repulsive potentials. We develop a new potential field method using dynamic agent internal states, allowing the swarm agents' internal states to manipulate the potential field. This new method successfully solves a reactive path planning problem that cannot be solved using static potential fields due to local minima formation. Simulation results demonstrate the ability of a swarm of agents that use the model to perform reactive problem solving effectively using the collective behaviour of the entire swarm in a way that matches studies based on real animal group behaviour.

Collaborative Multi-Robot Monte Carlo Localization in Assistant Robots

Abstract: This paper presents an algorithm for collaborative mobile robot localization based on probabilistic methods (Monte Carlo localization) used in assistant robots. When a root detects another in the same environment, a probabilistic method is used to synchronize each robot's belief. As a result, the robots localize themselves faster and maintain higher accuracy. The technique has been implemented and tested using a virtual environment capable to simulate several robots and using two real mobile robots equipped with cameras and laser range-finders for detecting other robots. The result obtained in simulation and with real robots show improvements in localization speed and accuracy when compared to conventional single-robot localization.

Emergent Behavior of Ant Colony System in Digital Images for Acquiring Edge Information

Abstract: A novel method of acquiring edge information in digital images is presented based on the emergent pattern of the behavior of artificial ant swarm in digital images. The perceptual graph is proposed to represent the relationship between adjacent image points. The ant colony system is applied to build the perceptual graph, based on which the edge information is obtained. The experimental results show that the artificial ant swarm can effectively perform edge information acquirement in digital image.

Self-stabilizing structure forming algorithms for distributed multi-robot systems

Abstract: Object transportation is an important emerging application of multirobotic swarm systems. It requires a large number of robots to be dynamically coordinated in real-time to form structures around any given rigid body that is to be lifted and transported. This paper systematically investigates the major issues that need to be addressed in methods of dynamically forming robust transportation structures. Two self-stabilizing algorithms are developed to enable a swarm of robots to form a structure to handle object transportation. Even with only a limited localized view of the environment, the algorithm enables individual robots to cooperatively form a safe structure. The stability and fault-tolerance properties of the algorithms are formally proven. The performance of the self-stabilizing algorithms, in terms of their efficiency of convergence, is evaluated via experimental studies and the results show that the system can achieve the goals in real-time.