Showing papers on "Ant robotics published in 2005"

The I-SWARM project: Intelligent small world autonomous robots for micro-manipulation

Abstract: This paper presents the visions and initial results of the I-SWARM project funded by the European Commission. The goal of the project is to build the first very large-scale artificial swarm (VLSAS) with a swarm size of up to 1,000 micro-robots with a planned size of 2 x 2 x 1 mm 3 . First, the motivation for such a swarm is described and then first considerations and issues arising from the robots' size resembling artificial ants and the MST approach taken to realize that size are given. The paper will conclude with a list of possible scenarios inspired by biology for such a robot swarm.

On Formal Specification of Emergent Behaviours in Swarm Robotic Systems

Abstract: It is a characteristic of swarm robotics that specifying overall emergent swarm behaviours in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation we need such specifications. This paper explores the use of temporal logic to formally specify, and possibly also prove, the emergent behaviours of a robotic swarm. The paper makes use of a simplified wireless connected swarm as a case study with which to illustrate the approach. Such a formal approach could be an important step toward a disciplined design methodology for swarm robotics.

Ant Colony Algorithms for Steiner Trees: An Application to Routing in Sensor Networks

Abstract: This chapter introduces ant colony optimization as a method for computing minimum Steiner trees in graphs. Tree computation is achieved when multiple ants, starting out from different nodes in the graph, move towards one another and ultimately merge into a single entity. A distributed version of the proposed algorithm is also described, which is applied to the specific problem of data-centric routing in wireless sensor networks. This research illustrates how tree based graph theoretic computations can be accomplished by means of purely local ant interaction. The authors hope that this work will demonstrate how innovative ways to carry out ant interactions can be used to design effective ant colony algorithms for complex optimization problems. This chapter appears in the book, Recent Developments in Biologically Inspired Computing, edited by Leandro N. de Castro and Fernando J. Von Zuben. Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING 182 Singh, Das, Gosavi & Pujar Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. INTRODUCTION Ants live in colonies and have evolved to exhibit very complex patterns of social interaction. Such interactions are clearly seen in the foraging strategy of ants. Despite the extremely simplistic behavior of individual ants, they can communicate with one another through secretions called pheromones, and this cooperative activity of the ants in a nest gives rise to an emergent phenomenon known as swarm intelligence (Bonabeau et al., 1999). Ant Colony Optimization (ACO) algorithms are a class of algorithms that mimic the cooperative behavior of real ant behavior to achieve complex computations. Ant colony optimization was originally introduced as a meta-heuristic for the wellknown traveling salesman problem (TSP), which is a path based optimization problem. This problem is proven to be NP-complete, which is a subset of a class of difficult optimization problems that are not solvable in polynomial time (unless P=NP). Since an exponential time algorithm is infeasible for larger scale problems in class NP, much research has focused on applying stochastic optimization algorithms such as genetic algorithms and simulated annealing to obtain good (but not necessarily globally optimal) solutions. The ant colony approach was subsequently shown to be a very effective technique for approaching a variety of other combinatorial optimization problems in class NP. An intrinsic advantage of ACO is the relative ease of implementation in a decentralized environment. These algorithms have therefore been applied to distributed network based problems that involve optimal path computations, such as routing, load balancing, and multicasting in computer networks (Bonabeau et al., 1998; Das et al., 2002; Navarro-Varela & Sinclair, 1999; Schoonderwoerd, 1997). In the rest of this chapter, we will use the terms distributed algorithm, online algorithm and decentralized algorithm interchangeably to imply algorithms that do not require any form of global computation. Algorithms that do require it will be referred to as centralized, or offline algorithms. This chapter explores the application of ant colony algorithms to the data-centric routing in sensor networks. This problem involves establishing paths from multiple sources in a sensor network to one or more destinations, where data are aggregated at intermediate stages in the paths for optimal dissemination. When only a single destination is involved, the optimal path amounts to a minimum Steiner tree in the sensor network. The minimum Steiner tree problem is a classic NP-complete problem that has numerous applications. It is a problem of extracting a sub-tree from a given graph with certain properties. A formal description of the problem is postponed until later. The second section introduces the ant colony optimization approach. The Steiner tree problem is introduced here and its applicability to sensor networks taken up in detail. The third section provides the details of the algorithm. It first describes an offline algorithm that can be used to compute Steiner trees of any graph. A preliminary set of simulations carried out to demonstrate the algorithm’s effectiveness is included. This is followed in the fourth section by a detailed description of the online algorithm to establish optimal paths for data-centric routing. Simulation results for three separate randomly generated networks are analyzed. In the fifth section, further extensions and applications of the present algorithm are suggested. Conclusions are provided in the last section. 24 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/chapter/ant-colony-algorithms-steinertrees/28328?camid=4v1 This title is available in InfoSci-Books, InfoSci-Medical, Communications, Social Science, and Healthcare. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=1

Self-Regulated Artificial Ant Colonies on Digital Image Habitats

Abstract: Artificial life models, swarm intelligent and evolutionary computation algorithms are usually built on fixed size populations Some studies indicate however that varying the population size can increase the adaptability of these systems and their capability to react to changing environments In this paper we present an extended model of an artificial ant colony system designed to evolve on digital image habitats We will show that the present swarm can adapt the size of the population according to the type of image on which it is evolving and reacting faster to changing images, thus converging more rapidly to the new desired regions, regulating the number of his image foraging agents Finally, we will show evidences that the model can be associated with the Mathematical Morphology Watershed algorithm to improve the segmentation of digital grey-scale images KEYWORDS: Swarm Intelligence, Perception and Image Processing, Pattern Recognition, Mathematical Morphology, Social Cognitive Maps, Social Foraging, Self-Organization, Distributed Search

Swarm robotics : SAB 2004 International Workshop, Santa Monica, CA, USA, July 17, 2004 : revised selected papers

Abstract: From Swarm Intelligence to Swarm Robotics.- Swarm Robotics: From Sources of Inspiration to Domains of Application.- Communication, Diversity and Learning: Cornerstones of Swarm Behavior.- The SWARM-BOTS Project.- Pheromone Robotics and the Logic of Virtual Pheromones.- Distributed Localization and Mapping with a Robotic Swarm.- The I-SWARM Project: Intelligent Small World Autonomous Robots for Micro-manipulation.- An Overview of Physicomimetics.- Lattice Formation in Mobile Autonomous Sensor Arrays.- Swarming Behavior Using Probabilistic Roadmap Techniques.- Towards Dependable Swarms and a New Discipline of Swarm Engineering.- A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems.- Order by Disordered Action in Swarms.

A Miniature Mobile Robot With a Color Stereo Camera System for Swarm Robotics Research

Abstract: In swarm robotics research, instead of using large size robots, it is often desirable to have multiple small size robots for saving valuable work space and making the maintainance of the robots easier. Also, the implementation costs of a miniature robot is lower because of simpler mechanical design. In this paper, we present a novel modular miniature mobile robot designed for swarm robotics research. The sensor set of the robot includes a color stereo camera system with two CMOS cameras and DSP, allowing each robot to do sophisticated stereo image processing on-board. The modular design permits the addition of new modules into the system. The modules communicate using three serial buses (SPI, I2C, and UART), which enable flexible, adaptive, and fast inter-module data exchange. The robot is developed for swarm robotics research with the aim to provide a low-cost and low-power miniature mobile robot with capabilities typically found only in large size robots.

Research Development of Bio-inspired Robotics

Abstract: Based on the classification of bio-inspired robots, this paper describes some bio-inspired robots which have been used or are under development. Furthermore, the future of bio-inspired robots is forecastd.

Autonomous navigation system applied to collective robotics with ant-inspired communication

Abstract: Research in collective robotics is motivated mainly by the possibility of achieving an efficient solution to multi-objective navigation tasks when multiple robots are employed, instead of a single robot. Several approaches have already been tried in multi-robot systems, but the bio-inspired ones are the most frequent. This paper proposes to augment an autonomous navigation system based on learning classifier systems for using in collective robotics, introducing an inter-robot communication mechanism inspired by ant stigmergy, with each robot acting independently and cooperatively. The navigation system has no innate basic behavior and all knowledge necessary to compose the decision-making artifact is evolved as a function of the environmental feedback only, during navigation. Repulsive and/or attractive pheromone trails are produced by the robots along navigation, following very simple rules. Basically, each robot has to perform obstacle avoidance and target search, and the status of the pheromone level at the position currently occupied by each robot will influence the coordination of the two fundamental behaviors. Experiments are performed in simulation, with comparative results indicating that the presence of the pheromone trails is responsible for significant improvements in the capture rate and in the length of the route adopted by each robot.

Collision-Free path planning for mobile robots using chaotic particle swarm optimization

Abstract: Path planning for mobile robots is an important topic in modern robotics studies. This paper proposes a new approach to collision-free path planning problem for mobile robots using the particle swarm optimization combined with chaos iterations. The particle swarm optimization algorithm is run to get the global best particle as the candidate solution, and then local chaotic search iterations are employed to improve the solution precision. The effectiveness of the approach is demonstrated by three simulation examples.

A Survey of Swarm Intelligence

Abstract: A survey of swarm intelligent system is presented.Large amount of simple individuals have more advantages over a single complex one.Thus,swarm intelligence provides a new way to solve complex problems without global control.Firstly the prolific research results in this field are illustrated involving typical algorithms,applications and swarm-based robotics.On the basis of that,the future directions in this field are analyzed.Finally,the significance of study on swarm intelligence is pointed out in the conclusion.

Distributed localization and mapping with a robotic swarm

Abstract: We describe a project where behaviors of robot swarms are designed and studied for use in a distributed mapping domain. Behaviors are studied in both simulation and physical robots. We discuss the advantages and challenges of swarm robotics, in general and specific to our research. Software implementations and algorithms are introduced, as well as methodologies for the creation and assessment of swarm behaviors.

Signal Strength Coordination for Cooperative Mapping

Abstract: : This work addresses the problem of coordinating a team of mobile robots such that they form a connected ad-hoc wireless network while addressing task objectives. Many tasks, such as exploration or foraging, can be performed more efficiently when robots are able to communicate with each other. All or parts of these tasks can be performed in parallel, thus multiple robots can complete the task more quickly than a single robot. Communication and coordination among the robots can prevent robots from duplicating the effort of other robots, allowing the team to address the task more efficiently. In non-trivial environments, maintaining communication can be difficult due to the unpredictable nature of wireless signal propagation. We propose a multi-robot coordination method based on perceived wireless signal strength between cooperating robots for exploration in maze-like environments. This new method is tested and compared to an existing method that relies on preserving a clear line of sight between robots to maintain communication.

On the Development of a Swarm Engineering Methodology

Abstract: This paper explores swarm engineering by revisiting popular concepts from swarm intelligence and making them more rigorous by providing mathematical definitions. The definitions form the basis for an examination of an engineering methodology which starts by examining the desired state of a global property of the system and then generates a requirement for a local behavior that generates the global property. This methodology allows a local behavior to be tested theoretically before it is tested empirically

Multi-agent cooperation using the ant algorithm with variable pheromone placement

Abstract: The ant algorithm was created by examining real life ant colonies and developing an algorithm to use the concept of "stigmergy" to approach multi-agent problems with distributed control. As agents work on tasks, more agents attempt difficult tasks. Task deadlock occurs when agents attempt impossible tasks indefinitely. Previous research avoids task deadlock through adaptive attenuation factors. This research investigates increasing algorithm effectiveness through variable pheromone placement. Results of computational experiments are presented demonstrating the increased effectiveness of the new algorithm.

News: Small Robots Team Up to Tackle Large Tasks

Abstract: Swarm robotics, a subset of multiagent systems research, focuses on very large teams of small robots working together toward a common goal. The robots could eventually be engineered to nanoscale size—invisible to the human eye—and number in the hundreds, thousands, or tens of thousands per group. David Payton, a research scientist at HRL Laboratories ( ), expects these robot groups to become more effective as their numbers increase.

Fusing vision-based bearing measurements and motion to localize pairs of robots

Abstract: —This paper presents a method to cooperativelylocalize pairs of robots fusing bearing-only information pro-vided by a camera and the motion of the vehicles. Thealgorithm uses the robots as landmarks to estimate therelative location between the platforms. Bearings are obtaineddirectly from the camera, as opposed to measuring depthswhich would require knowledge or reconstruction of the worldstructure.We present the general recursive Bayes estimator and threedifferent implementations based on an extended Kalman filter,a particle filter and a combination of both techniques. Wehave compared the performance of the different implemen-tations using real data acquired with two platforms, oneequipped with an omnidirectional camera, and simulateddata.Keywords: Cooperative robots, bearing-only measure-ments, localization. I. I NTRODUCTION In the last years cooperative robotics has received con-siderable attention. Teams of robots are able to overcomethe limitations of single robots and allow to attack moredifficult tasks. Furthermore, they increase the degree ofautonomy and robustness by introducing redundancy. How-ever, the use of teams of robots increases the complexity ofthe system. New challenges appear providing new researchareas. In this context cooperative localization is consideredone of the basic capabilities required for autonomousoperation of teams of robots.In this paper we address the problem of cooperativelylocalizing two robots using bearing-only measurementsand the motion of the robots. When the robots navigatebased on proprioceptive sensors, e.g. odometry, they buildand maintain their own, unrelated, referential frames. Byproviding exteroceptive sensors, such as omnidirectionalcameras that track other robots, the various referentialframes can be fused to a single one. The common ref-erential frame opens the way for cooperation and sharingof the information acquired by each of the robots.Omnidirectional cameras allow localizing robots in a360

Swarm intelligence for network routing optimization

Abstract: This paper presents the results of a comparative study of network routing approaches. Recent advances in the field suggest that swarm intelligence may offer a robust, high quality solution. The overall aim of the study was to develop a framework to facilitate the empirical evaluation of a swarm intelligence routing approach compared to a conventional static and dynamic routing approach. This paper presents a framework for the simulation of computer networks, collection of performance statistics, generation and reuse of network topologies and traffic patterns. Keywords— network routing, swarm intelligence, ant algorithms.

Ant colony search algorithms for optimal packing problem

Abstract: Ant Colony optimization takes inspiration from the behavior of real ant colony to solve optimization problems. This paper presents a parallel model for ant colony to solve the optimal packing problem. The problem is represented by a directed graph so that the objective of the original problem becomes to find the shortest closed circuit on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The algorithm supports the parallel computation and facilitates quick convergence to the optimal solution. The performance of the proposed method as compared to those of the genetic-based approaches is very promising.

Effectiveness of Emerged Pheromone Communication in an Ant Foraging Model

Abstract: The collective behavior of social insects has been a puzzling problem for scientists for a long time. In particular, it is well known that ants solve difficult problems, for instance selecting the shortest pathway by communicating with each other via pheromone. How is it possible for such simple creatures to coordinate their behaviors and to solve problems as a whole? This paper focuses on the emergence of the pheromone communication system based on an ant foraging model in which neural networks of ant agents evolve according to the result of foraging. The computer experiments show that the ant agents using emerged communication with one type of pheromone are more adaptive than the ant agents not using pheromone communication or the ant agents using human-designed communication with 2 types of pheromone. This paper also discusses the reason for this superiority of the evolved pheromone communication.

Evolutionary swarm intelligence applied to robotics

Abstract: In this paper, we discuss some techniques for achieving swarm intelligent robots through the use of traits of personality. Traits of personality are characteristics of each robot that, altogether, define the robot's behaviours. We discuss the use of evolutionary psychology in order to select a set of traits of personality that evolve due to a learning process based on reinforcement learning. The use of game theory is introduced and a simulation showing its potential is reported.

Application of the ant colony algorithm for the path planning

Abstract: The paper presents the ant colony algorithm and its application for the path planning. Ant algorithms were designed on the base of the behaviour of real ant colonies. Real ants can always find the shortest way between the nest and the food so one of the most "natural" is the application of the ant colony algorithm in the path planning. Described algorithm was implemented in some off-line experiments.

The Application of Swarm Engineering Technique to Robust Multi-chain Robot System

Abstract: The swarm engineering technique construed attempts to develop a general methodology that can be applied in creating swarm-mediated systems. In this companion paper, we utilize the system established to further explore the swarm engineering technique and show how this methodology can be used to develop a robust multi-chain robot system in a rigorous manner. In addition, we show the benefits of building a multi-chain robot system using the swarm engineering technique

Intelligent ant colony algorithm

Abstract: Ant colony algorithm is a novel simulating evolution algorithm with typical swarm intelligence feature and is used to solve some complicated NP-hard combinatorial optimization problems.This algorithm is applied to a lot of fields triumphantly,for its several characteristics,such as positive feedback,distributed computing,robustness and parallelism.Principium,characteristics and improved modes of ant colony algorithm are described generally.

Minimal communication design of embodied interface

Abstract: Robots have been envisaged as both workers and partners of humans from the earliest period of their history. Therefore, robots should become artificial entities that can potentially socially interact with human beings in social communities. Recent advances in technology have added various functions to robots: Development of actuators and grippers show us infinite possibilities for factory automation, and robots can now walk and perform very smoothly. All of these functions have been developed as solutions improving to robot movement and performance. However, there are many remaining problems in the communication between robots and humans. Communication robots provide one approach to realization of embodied interfaces. These unsolved problems involving communication can be clarified by adopting the concept of subtractive methods. In this paper, we consider the minimal design of robots from the viewpoint of designing communication. By minimal design, we mean eliminating the non-essential portions and keeping only the most fundamental functions. We expect that the simple and clean nature of minimally designed objects will allow humans to interact with these robots without becoming uninterested too quickly. Because humans have "a natural dislike for the absence of reasoning", artificial entities built according to minimal design principles have the ability to extract the human drive to relate with others. We propose a method of designing a robot that has "character" and is situated in a social context from the viewpoint of minimal design.

Collaborative exploration for a group of self-interested robots

Abstract: This paper presents and new approach to robot exploration and mapping using a team of cooperative robots. This approach aims to exploit the increase in sensor data that multiple robots offer to improve efficiency, accuracy and detail in maps created, and also the lower cost in employing a group of inexpensive robots. The exploration technique involves covering an area as efficiently as possible while cooperating to estimate each other's positions and orientations. The ability to observe objects of interest from a number of viewpoints and combine this data means that cooperative robots can localize objects and estimate their shape in cluttered real world scenes. Robots in the system act as social agents, and are motivated to cooperate by a desire to increase their own utility. Within this society, robots form coalitions to complete tasks that arise which require input from multiple robots. The coalitions involve the adoption of certain roles or behaviors on the part of the different robots to carry out these tasks.

Swarm intelligence beyond stigmergy: Traffic optimization in ants

Abstract: Swarm intelligence is widely recognized as a powerful paradigm of self-organized optimization. Whereas the stigmergy concept of information exchange has revealed a vast body of successful applications in distributed artificial intelligence, the role of physical interactions in swarm intelligence was explored only recently. Here we show that physical encounters in ant traffic play a vital role in the optimization of transport processes in ant colonies, if the capacities of transport ways are limited. In crowded situations ant traffic is optimized through a balanced interaction of pheromone guided trail following behavior and a congestion driven rerouting of ants. This points towards a promising design-principle for self-optimizing artificial transport systems.

Cooperative Mobile Robots

Abstract: Software for the exploration task and the search task on multiple heterogeneous robots has been developed in this Master’s Thesis. The robots build a map of the environment using low-cost IR range finders and search for red objects in the area using a webcam. Furthermore, to extend the sensing capabilities of the robots, a bump sensor has been implemented. Communication is carried out on a P2P network for fault tolerance. The P2P network is based on JXTA and robots can join and leave the team dynamically. Path planning is conducted using the D• Lite algorithm and a new method for smoothening and shortening this path is proposed. The robots are coordinated such that they distribute themselves in the environment without relying on a central agent and select new areas to explore in a greedy manner. A human agent can participate in the team. This agent receives pictures of red objects found by the robots for verification, ie. the human agent might only be interested in a specific kind of object. A multi-robot simulator has been implemented. All relevant robot components have been modeled, in particular sensors and actuators. New environments are inserted in the form of a drawn bitmap or a photo. The solution has been tested on real mobile robots and simulated. The software is able to run locally on the robots, ie. on lowend PCs. The result of the simulation shows that the software scales to many robots.

A Test-Environment for Control Schemes in the Field of Collaborative Robots and Swarm Intelligence

Abstract: This paper presents an architecture for a testenvironment for algorithms and control schemes in the field of collaborative robotics and swarm intelligence. As the foundation of the testenvironment, small bionically inspired robots are presented. The robots are small (20 cm x 5 cm x 5 cm) and lightweight (< 200 g). Their design is inspired by the movement of caterpillars. Three cubical segments are connected via special joints, where each of these joints has three independent degrees of translatory freedom. Thus, the robots are able to handle rough terrain with small obstacles. The robots are driven by innovative piezoelectric motors that allow a gearless design without any rotary parts. Each robot is equipped with on-board processing and radio communication. The software of the robots is written using TinyOS, an eventdriven operating system for large-scale distributed sensor-actuator-networks.