DOCSLIB.ORG

Cooperative Cleaners: a Study in Ant Robotics

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Cooperative Cleaners: a Study in Ant Robotics Israel A. Wagner Cooperative Cleaners: Computer Science Department Technion, Haifa 32000, Israel A Study in Ant Robotics [email protected] and IBM Haifa Labs, MATAM, Haifa 31905, Israel [email protected] Yaniv Altshuler Vladimir Yanovski Alfred M. Bruckstein Computer Science Department Technion, Haifa 32000, Israel Abstract of robots without a central supervisor, using only local inter- actions between the robots. When this decentralized approach In the world of living creatures, simple-minded animals often coop- is used, much of the communication overheads (characteristic erate to achieve common goals with amazing performance. One can of centralized systems) are saved, the hardware of the robots consider this idea in the context of robotics, and suggest models for can be fairly simple and better modularity is achieved. A prop- programming goal-oriented behavior into the members of a group of erly designed system should achieve reliability through redun- simple robots lacking global supervision. This can be done by con- dancy. trolling the local interactions between the robot agents, to have them Significant research effort has been invested during the last jointly carry out a given mission. As a test case we analyze the prob- few years in design and simulation of multi-agent systems lem of many simple robots cooperating to clean the dirty floor of a (Brooks 1990 Steels 1990 Levi 1992 Sen et al. 1994 Arkin non-convex region in Z2, using the dirt on the floor as the main means and Balch 1998 Wagner et al. 1998 Wagner and Bruckstein of inter-robot communication. 2001). Such designs are often inspired by biology (see Brooks 1986 Arkin 1990 Arkin and Balch 1997 for behavior-based KEY WORDS—robotics, covering, multirobotics, robustness, control models, Deneubourg et al. 1990 Drogoul and Fer- cooperative robotics ber 1992 Mataric 1992 for flocking and dispersing models or Benda et al. 1985 Haynes and Sen 1986 for predator–prey approach). Inspiration has also been found in physics (e.g. 1. Introduction Chevallier and Payandeh 2000) and the application of eco- nomics (see e.g. Smith 1980 Golfarelli et al. 1997 Wellman In the world of living creatures, simple-minded animals such and Wurman 1998 Rabideau et al. 1999 Thayer et al. 2000 as ants or birds cooperate to achieve common goals with sur- Gerkey and Mataric 2002 Zlot et al. 2002). prising performance. It seems that these animals are ‘pro- Tasks that have been of particular interest to researchers grammed’ to interact locally in such a way that the desired in recent years include synergetic mission planning (Alami global behavior is likely to emerge even if some individuals et al. 1997), fault tolerance (Parker 1998), swarm control of the colony die or fail to carry out their task for some other (Mataric 1994), human design of mission plans (MacKenzie reasons. We consider a similar approach to coordinate a group et al. 1997), role assignment (Stone and Veloso 1999 Can- dea et al. 2001 Pagello et al. 2003), multi-robot path planning The International Journal of Robotics Research (Lumelsky and Harinarayan 1997 Ferrari et al. 1998 Svestka Vol. 27, No. 1, January 2008, pp. 127–151 and Overmars 1998 Yamashita et al. 2000), formation gener- DOI: 10.1177/0278364907085789 ation (Arai et al. 1989 Fredslund and Mataric 2001 Gordon c SAGE Publications 2008 Los Angeles, London, New Delhi and Singapore Figure 16 appears in color online: http://ijr.sagepub.com et al. 2003), traffic control (Premvuti and Yuta 1990), forma- 127 128 THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH / January 2008 tion keeping (Wang 1989 Balch and Arkin 1998), exploration tolerance. Even if almost all the agents cease to work before and mapping (Rekleitis et al. 2003), target tracking (Parker and completion, the remaining agents will eventually complete the Touzet 2000) and target search (LaValle et al. 1997). mission. We prove the correctness of the algorithm as well as Unfortunately, the geometrical theory of such multi-agent an upper bound on its running time, and show how our algo- systems is far from being satisfactory, as has been pointed out rithm can be extended for regions with obstacles. in Beni and Wang (1991) and many other articles. A short The rest of the paper is organized as follows. In Section 3 discussion regarding the conceptual framework of intelligent the formal problem is defined as well as the aims and assump- swarms (or swarm intelligence systems) and the motivation be- tions involved. The algorithm is presented in Section 4, and an hind the use of such appears in Section 2. analysis of its time-complexity is described in Section 5. Sec- Our interest is focused on developing the mathematical tion 6 is devoted to the problem of exploring/cleaning regions tools necessary for the design and analysis of such systems. In with obstacles, and is followed by several experimental exam- a recent report (Wagner and Bruckstein 1994) we have shown ples (Section 7). We conclude the paper with a discussion and that a number of agents can arrange themselves equidistantly by highlighting several connections to related work, presented in a row via a sequence of linear adjustments, based on a sim- in Section 8. ple ‘local’ interaction. The configuration convergence is expo- This paper is a revised and extended version of the work nentially fast. A different way of cooperation between agents presented in Wagner and Bruckstein (1997). is inspired by the behavior of ant colonies, and is described by Bruckstein (1993). There it was proved that a sequence of ants engaged in deterministic chain pursuit will find the short- 2. Swarm Intelligence: Framework and est (i.e. straight) path from the anthill to the food source, using Motivation only local interactions. In Bruckstein et al. (1994) we investi- gate the behavior of a group of agents on Z2, where each ant- 2.1. Motivation like agent is pursuing his predecessor according to a discrete biased random-walk model of pursuit on the integer grid. We Robotics solutions to increasingly complex and varied chal- proved that the average paths of such a sequence of a(ge)nts lenges are growing in demand. Experience has dictated that a engaged in a chain of probabilistic pursuit converge to the single robot is no longer the best solution for many application straight line between the origin and destination, and this hap- domains. Instead, teams of robots must coordinate intelligently pens exponentially fast. for successful task execution. In this paper we investigate a more complicated question Dias and Stentz (2001) present a detailed description of concerning the behavior of many agents cooperating to explore multi-robot application domains and demonstrate how multi- an unknown area (for purposes of cleaning, painting, etc.), robot systems can be more effective than a single robot in where each robot/agent can only see its near neighborhood. many of these domains. However, when designing such sys- The only method of inter-robot communication is by leaving tems it should be noticed that simply increasing the number traces on the common ground and sensing the traces left by of robots assigned to a task does not necessarily improve the other robots. See Min and Yin (1998) Acar et al. (2001) But- system’s performance. Multiple robots must cooperate intel- ler et al. (2001) Koenig and Liu (2001) Polycarpou et al. ligently to avoid disturbing each other’s activity and achieve (2001) Batalin and Sukhatme (2002) Latimer et al. (2002) efficiency. Rekleitisy et al. (2004) for similar research. There are several key advantages of the use of such intelli- We present an algorithm for cleaning a dirty area that guar- gent swarm robotics. First, such systems inherently enjoy the antees task completion (unless all robots die) and prove an up- benefit of parallelism. In task-decomposable application do- per bound on the time complexity of this algorithm. We also mains, robot teams can accomplish a given task more quickly show simulation results of the algorithm on several types of re- than a single robot by dividing the task into sub-tasks and ex- gions. These simulations indicate that the precise time depends ecuting them concurrently. In certain domains, a single robot on the number of robots, their initial locations and the shape may simply be unable to accomplish the task on its own (e.g. of the region. carrying a large and heavy object). Second, decentralized sys- In the spirit of Braitenberg (1984), we consider simple tems tend to be, by their very nature, much more robust than robots with only a bounded amount of memory (i.e. finite- centralized systems (or systems comprised of a single com- state machines). Generalizing an idea from computer graph- plex unit). Generally speaking, a team of robots may provide a ics (see Henrich 1994), we preserve the connectivity of the more robust solution by introducing redundancy and by elim- ‘dirty’ region by allowing an agent to clean only so-called inating any single point of failure. While considering the al- non-critical points: points that do not disconnect the graph of ternative of using a single sophisticated robot, we should note dirty grid points. This ensures that the robots will stop only that even the most complex and reliable robot may suffer an upon completing their mission. An important advantage of this unexpected malfunction which will prevent it from complet- approach, in addition to the simplicity of the agents, is fault- ing its task. When using a multi-agents system, on the other Wagner, Altshuler, Yanovski, and Bruckstein / Cooperative Cleaners: A Study in Ant Robotics 129 hand, even if a large number of the agents stop working for Computational resources: Although agents are assumed some reason the entire group will often be able to complete to employ only limited computational resources, a for- its task (albeit slower).
Load more

Recommended publications
  • Make Robots Be Bats: Specializing Robotic Swarms to the Bat Algorithm
    © <2019>. This manuscript version is made available under the CC- BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc- nd/4.0/ Accepted Manuscript Make robots Be Bats: Specializing robotic swarms to the Bat algorithm Patricia Suárez, Andrés Iglesias, Akemi Gálvez PII: S2210-6502(17)30633-8 DOI: 10.1016/j.swevo.2018.01.005 Reference: SWEVO 346 To appear in: Swarm and Evolutionary Computation BASE DATA Received Date: 24 July 2017 Revised Date: 20 November 2017 Accepted Date: 9 January 2018 Please cite this article as: P. Suárez, André. Iglesias, A. Gálvez, Make robots Be Bats: Specializing robotic swarms to the Bat algorithm, Swarm and Evolutionary Computation BASE DATA (2018), doi: 10.1016/j.swevo.2018.01.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT Make Robots Be Bats: Specializing Robotic Swarms to the Bat Algorithm Patricia Su´arez1, Andr´esIglesias1;2;:, Akemi G´alvez1;2 1Department of Applied Mathematics and Computational Sciences E.T.S.I. Caminos, Canales y Puertos, University of Cantabria Avda. de los Castros, s/n, 39005, Santander, SPAIN 2Department of Information Science, Faculty of Sciences Toho University, 2-2-1 Miyama 274-8510, Funabashi, JAPAN :Corresponding author: [email protected] http://personales.unican.es/iglesias Abstract Bat algorithm is a powerful nature-inspired swarm intelligence method proposed by Prof.
    [Show full text]
  • Ants Meeting Algorithms
    Ants Meeting Algorithms Asaf Shiloni, Alon Levy Ariel Felner, Meir Kalech The MAVERICK Group Department of Information System Engineering Computer Science Department Deutsche Telekom Labs Bar-Ilan University, Israel Ben-Gurion University, Israel {asafshiloni,alonlevy1}@gmail.com {felner,kalech}@bgu.ac.il ABSTRACT Furthermore, ants were shown to be practical in real-world applica- Ant robots have very low computational power and limited memory. They tions. For example, the advantage of ants over convectional robots communicate by leaving pheromones in the environment. In order to create in practice is found in the micron-scale environments such as the a cooperative intelligent behavior, ants may need to get together; however, blood stream and other parts of the human body. The robots acting they may not know the locations of other ants. Hence, we focus on an ant in these environments cannot be equipped with abundant memory or high-end sensors, nor with high computational performance ca- variant of the rendezvous problem, in which two ants are to be brought to the same location in finite time. We introduce two algorithms that solve pabilities. Thus, only robots with light hardware capabilities must this problem for two ants by simulating a bidirectional search in different be used [5, 6]. environment settings. An algorithm for an environment with no obstacles Algorithms for ant robots often require cooperation between mul- and a general algorithm that handles all types of obstacles. We provide tiple ants. These algorithms usually assume that the ants start ex- detailed discussion on the different attributes, size of pheromone required, ecuting the algorithm while located at the same location, or in a and the performance of these algorithms.
    [Show full text]
  • Stigmergic Masa: a Stigmergy Based Algorithm 1479
    Proceedings of the 2014 Federated Conference on DOI: 10.15439/2014F395 Computer Science and Information Systems pp. 1477–1485 ACSIS, Vol. 2 S-MASA: A Stigmergy Based Algorithm for Multi-Target Search Ouarda Zedadra and Hamid Seridi Nicolas Jouandeau Giancarlo Fortino LabSTIC Laboratory, 8 may 1945 University Advanced Computing laboratory DIMES, Universita’ P.O.Box 401, 24000 Guelma, Algeria of saint-Denis Paris 8 University della Calabria Department of computer science Badji Mokhtar Saint Denis 93526, France Via P. Bucci, cubo 41c - 87036 Annaba University P.O.Box 12, 23000 Annaba, Algeria Email: [email protected] - Rende (CS) - Italy Email: zedadra nawel1, [email protected] Email:[email protected] Abstract—We explore the on-line problem of coverage where 1) it is of very low computational complexity, in which multiple agents have to find a target whose position is unknown, agents have a very low-range of sensors; and without a prior global information about the environment. 2) it executes a search in nearby locations first by adopting In this paper a novel algorithm for multi-target search is described, it is inspired from water vortex dynamics and based spiral turns around the starting cell and around agents on the principle of pheromone-based communication. Accord- each other; ing to this algorithm, called S-MASA (Stigmergic Multi Ant 3) agents use stigmergic communication via digital Search Area), the agents search nearby their base incrementally pheromone; using turns around their center and around each other, until 4) it can be executed on known or unknown static obstacle- the target is found, with only a group of simple distributed cooperative Ant like agents, which communicate indirectly via free environments or obstacle environments.
    [Show full text]
  • Control of Ad-Hoc Formations for Autonomous Airport Snow Shoveling
    The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems October 18-22, 2010, Taipei, Taiwan Control of ad-hoc formations for autonomous airport snow shoveling Martin Saska and Vojtechˇ Vonasek´ and Libor Preuˇ cilˇ Abstract— In this paper, we present a framework that applies example of decentralized cleaning algorithm is a method multiple groups of autonomous snowploughs for efficiently inspired by cooperative behavior of ants introduced in [10]. removing the snow from airfields. The proposed approach in- Projecting the problem of cooperative sweeping onto the cludes formation stabilization into variable shapes depending on the width of runways. The paper is focused on trajectory plan- airfield, the snow shoveling task generates some problems ning and control during splitting and coupling of formations that have not yet been solved. Our approach considers the for cleaning smaller auxiliary roads surrounding main runways. nonholonomic kinematics of usual snowploughs as well We propose a general method using a receding horizon control as the position and orientation of the ploughs’ shovels. for iterative formation assignment. The algorithm is adapted for Furthermore, the working space is more structured due to the kinematics of car-like robots and can be utilized in arbitrary static and dynamic airport assemblage. The proposed approach the airfield environment. has been verified by simulations and by hardware experiments. As was mentioned above, the safety requirements at the airport predestinates the utilization of autonomous forma- I. INTRODUCTION tions, which has not yet been investigated in terms of sweep- The main task of the ground staff at the airport is to ing. In the classical literature, formation driving approaches maintain airports’ operations safe and uninterrupted.
    [Show full text]
  • Of Robot Ants and Elephants: a Computational Comparison
    Theoretical Computer Science 412 (2011) 5771–5788 Contents lists available at SciVerse ScienceDirect Theoretical Computer Science journal homepage: www.elsevier.com/locate/tcs Review Of robot ants and elephants: A computational comparison Asaf Shiloni ⇤, Noa Agmon, Gal A. Kaminka The MAVERICK Group, Computer Science Department, Bar Ilan University, Israel article info abstract Article history: In the robotics community, there exist implicit assumptions concerning the computational Received 19 July 2010 capabilities of robots. Two computational classes of robots emerge as focal points of recent Received in revised form 27 April 2011 research: robot ants and robot elephants. Ants have poor memory and communication Accepted 22 June 2011 capabilities, but are able to communicate using pheromones, in effect, turning their work Communicated by G. Ausiello area into a shared memory. By comparison, elephants are computationally stronger, have large memory, and are equipped with strong sensing and communication capabilities. Unfortunately, not much is known about the relation between the capabilities of these models in terms of the tasks they can address. In this paper, we present formal models of both ants and elephants, and investigate if one dominates the other. We present two algorithms: AntEater, which allows elephant robots to execute ant algorithms and ElephantGun, which converts elephant algorithms – specified as Turing machines – into ant algorithms. By exploring the computational capabilities of these algorithms, we reach interesting
    [Show full text]
  • Anticipating and Preparing for Emerging Skills and Jobs Key Issues, Concerns, and Prospects Education in the Asia-Pacific Region: Issues, Concerns and Prospects
    Education in the Asia-Pacific Region: Issues, Concerns and Prospects 55 Brajesh Panth Rupert Maclean Editors Anticipating and Preparing for Emerging Skills and Jobs Key Issues, Concerns, and Prospects Education in the Asia-Pacific Region: Issues, Concerns and Prospects Volume 55 Series Editors Rupert Maclean, RMIT University, Melbourne, Australia Lorraine Pe Symaco, Zhejiang University, Hangzhou, Zhejiang, China Editorial Board Bob Adamson, The Education University of Hong Kong, Hong Kong, China Robyn Baker, New Zealand Council for Educational Research, Wellington, New Zealand Michael Crossley, University of Bristol, Bristol, UK Shanti Jagannathan, Asian Development Bank, Manila, Philippines Yuto Kitamura, University of Tokyo, Tokyo, Japan Colin Power, Graduate School of Education, University of Queensland, Brisbane, Australia Konai Helu Thaman, University of the South Pacific, Suva, Fiji Advisory Editors Mark Bray, UNESCO Chair, Comparative Education Research Centre, The University of Hong Kong, Hong Kong, China Yin Cheong Cheng, The Education University of Hong Kong, Hong Kong, China John Fien, RMIT University, Melbourne, Australia Pham Lan Huong, International Educational Research Centre, Ho Chi Minh City, Vietnam Chong-Jae Lee, Korean Educational Development Institute (KEDI), Seoul, Korea (Republic of) Naing Yee Mar, GIZ, Yangon, Myanmar Geoff Masters, Australian Council for Educational Research, Melbourne, Australia Margarita Pavlova, The Education University of Hong Kong, Hong Kong, China Max Walsh, Secondary Education Project, Manila, Philippines Uchita de Zoysa, Global Sustainability Solutions (GLOSS), Colombo, Sri Lanka The purpose of this Series is to meet the needs of those interested in an in-depth analysis of current developments in education and schooling in the vast and diverse Asia-Pacific Region. The Series will be invaluable for educational researchers, policy makers and practitioners, who want to better understand the major issues, concerns and prospects regarding educational developments in the Asia-Pacific region.
    [Show full text]
  • Efficient Cooperative Search of Smart Targets Using UAV Swarms
    Robotica (2008) volume 26, pp. 551–557. © 2008 Cambridge University Press doi:10.1017/S0263574708004141 Printed in the United Kingdom Efficient cooperative search of smart targets using UAV Swarms1 Yaniv Altshuler†∗, Vladimir Yanovsky†, Israel A. Wagner†‡ and Alfred M. Bruckstein† †Computer Science Department, Technion, Haifa 32000 Israel. ‡IBM Haifa Labs, MATAM, Haifa 31905 Israel. (Received in Final Form: January 2, 2008. First published online: February 18, 2008) SUMMARY economics applications, and refs. [24, 35] for physics- This work examines the Cooperative Hunters problem, inspired approaches). where a swarm of unmanned air vehicles (UAVs) is used for Tasks that have been of particular interest to researchers searching one or more “evading targets,” which are moving in recent years include synergetic mission planning,8 fault in a predefined area while trying to avoid a detection by the tolerance,9 swarm control,10 human design of mission swarm. By arranging themselves into efficient geometric plans,11 role assignment,12 multi-robot path planning,13 flight configurations, the UAVs optimize their integrated traffic control,14 formation generation,15 formation keep- sensing capabilities, enabling the search of a maximal ing,16 exploration and mapping,17 cleaning18 and dynamic territory. cleaning19 and target tracking.26 Unfortunately, the mathematical geometrical theory of such multi-agent systems is far from being satisfactory, as KEYWORDS: Swarm Algorithm, Cooperative Search, pointed out in ref. [25] and many other papers. Shape Factor. One of the most interesting challenges for a robotics swarm system is the design and analysis of a multi-robotics system for searching areas (either known or unknown),27–31,34 or 1.
    [Show full text]
  • Swarm Intelligence — Searchers, Cleaners and Hunters
    Nadia Nedjah Luiza de Macedo Mourelle Swarm Intelligent Systems With 65 Figures and 34 Tables 123 Dr. Nadia Nedjah Department of Electronics Engineering and Telecommunications - DETEL Faculty of Engineering - FEN State University of Rio de Janeiro - UERJ Rua Sã o Franci s c o X a v i e r , 5 2 4 , 5 o. andar Maracanã , CEP 20559-900 Rio de Janeiro, RJ Brazil E-mail: [email protected] Dr. Luiza de Macedo Mourelle Department of System Engineering and Computation - DESC Faculty of Engineering - FEN State University of Rio de Janeiro - UERJ Rua Sã o Franci s c o X a v i e r , 5 2 4 , 5 o. andar Maracan ã , CEP 20559-900 Rio de Janeiro, RJ Brazil E-mail: [email protected] Library of Congress Control Number: 2006925434 ISSN print edition: 1860-949X ISSN electronic edition: 1860-9503 ISBN-10 3-540-33868-3 Springer Berlin Heidelberg New York ISBN-13 978-3-540-33868-0 Springer Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the mate- rial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recita- tion, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable to prosecution under the German Copyright Law.
    [Show full text]
  • Ant Robotics Стр
    Ant Robotics Стр . 1 из 4 additives | food ingredients | food additives | banned ingredients in food Dangerous Food Additives Source Guide | A | Ant robotics Bookmark this page Ant Robotics Related Topics FDA News Images : Ant Robotics FDA Warns Marketers of Unapproved Ant colony optimization Chelation Drugs Ants counter drugs have not been approved for chelation therapy. Pheromone Possible Fracture Risk With Osteoporosis Robots Drugs - The Food and Drug Administration Swarm Intelligence warns patients and health care providers about the possible risk of thigh bone Swarm robotics fracture in patients treated with bisphosphonates, a class of drugs used to prevent and treat osteoporosis. Consumer Update Analytics Sept. 30, 2010 - Sept. 17 - Sept. 30, 2010 Miracle Treatment Turns into Potent Bleach - A product marketed on the Internet as a treatment for many illnesses becomes a powerful bleaching agent when used as General Description directed. Ant robotics is a special case of swarm robotics. Infant Sleep Positioners Pose Suffocation Swarm robots are simple and cheap robots with Risk - Babies are at risk for suffocation limited sensing and computational capabilities. This when positioners are used in their cribs. makes it feasible to deploy teams of swarm robots Light Tobacco Products Pose Heavy Health and take advantage of the resulting fault tolerance Risks - Cigarettes labeled light, low, and and parallelism. Swarm robots cannot use mild are disappearing from the back wall of conventional planning methods due to their limited retail shops as health agencies work to sensing and computational capabilities. Thus, their inform smokers there s no such thing as a behavior is often driven by local interactions.
    [Show full text]
  • Modeling and Control of Multi-Elastic-Link Robots Under Gravity
    Modeling and Control of Multi-Elastic-Link Robots under Gravity From Oscillation Damping and Position Control to Physical Interaction DISSERTATION submitted in partial fulfillment of the requirements for the degree Doktor Ingenieur (Doctor of Engineering) in the Faculty of Electrical Engineering and Information Technology at TU Dortmund University by Dipl.-Ing. Jörn Malzahn Essen, Germany Date of submission: 10th February 2014 First examiner: Univ.-Prof. Dr.-Ing. Prof. h.c. Dr. h.c. Torsten Bertram Second examiner: Univ.-Prof. Dr.-Ing. Dr. h.c. Burkhard Corves Date of approval: 27th October 2014 Preface Robotics is a fascinating scientific discipline. By developing robots we assemble metal or plastic parts, wires as well as integrated circuits. Through software, we try to equip this assembly of lifeless components with a certain degree of apparent autonomy, which makes it move and interact with our environment. We struggle to convert basic movements and interactions into useful skills enabling e.g. robust locomotion on uneven terrain or dexterous manipulation of arbitrary objects. The harder we struggle, the more exciting becomes the moment, when a robot finally accomplishes some desired task. The struggle also completely changes our everyday perspective on how impressively rapid, easy and reliable humans learn and adapt to the various complex situations in life. Just look at young children starting their first grasping experiments... When I started studying electrical engineering at TU Dortmund University, my plan was to dive into this fascinating world of robotics with all its facets as a robot developer. While writing these lines I can look back and say the plan seems to have worked out pretty well so far.
    [Show full text]
  • Aggregation Behaviour As a Source of Collective Decision in a Group of Cockroach-Like-Robots0
    Aggregation behaviour as a source of collective decision in a group of cockroach-like-robots0 Simon Garnier1, Christian Jost1, Rapha¨el Jeanson1, Jacques Gautrais1, Masoud Asadpour2, Gilles Caprari2, and Guy Theraulaz1 1 Centre de Recherches sur la Cognition Animale, UMR-CNRS 5169, Universit´ePaul Sabatier, 118 route de Narbonne, F-31062 Toulouse cedex 4, FRANCE [email protected], 2 Autonomous Systems Lab, Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, SWITZERLAND Abstract. In group-living animals, aggregation favours interactions and information exchanges between individuals, and thus allows the emer- gence of complex collective behaviors. In previous works, a model of a self-enhanced aggregation was deduced from experiments with the cock- roach Blattella germanica. In the present work, this model was imple- mented in micro-robots Alice and successfully reproduced the agrega- tion dynamics observed in a group of cockroaches. We showed that this aggregation process, based on a small set of simple behavioral rules of interaction, can be used by the group of robots to select collectively an aggregation site among two identical or different shelters. Moreover, we showed that the aggregation mechanism allows the robots as a group to “estimate” the size of each shelter during the collective decision-making process, a capacity which is not explicitly coded at the individual level. 1 Introduction Since the last 15 years, collective robotics has undergone a considerable devel- opment [18]. In order to control the behavior of a group of robots, collective robotics was often inspired by the collective abilities demonstrated by social in- sects [3, 15]. Indeed, nature has already developed many strategies that solve collective problems through the decentralized organisation and coordination of many autonomous agents by self-organized mechanisms [4].
    [Show full text]
  • Assignment in COMPLEX ADAPTIVE SYSTEMS
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CiteSeerX UNIVERSITY OF ATHENS M.Sc. in BIOINFORMATICS ATHENS 2004 Assignment in COMPLEX ADAPTIVE SYSTEMS Topic Clustering in social insects Elpida Tzafestas 1 Objective of the project The objective is the implementation of a model of object clustering starting from the research work by Deneubourg and associates (cf. references). According to this model, which initially addresses ant or more generally insect societies, each ant or insect (agent) follows a very simple behavioral model : (Step 1) Random move to a neighboring position (Step 2) (a) If the agent does not carry food: if there is already food in the position of the agent, 2 then with a probability pc=(k1/(k1+f)) the agent charges a piece of food (b) Otherwise (if the agent carries a piece of food) 2 then with a probability pd=(f/(k2+f)) the agent discharges the piece k1 and k2 are constant ∈ (0,1) f the quantity (fraction) of food around the agent (Step 3) Update of variables used for action selection, here possibly the f variable. The above general case may be instantiated or further specialized in many ways : • Defining differently the neighbourhood of an agent ; it is generally a 4- ή 8- point neighbourhood, or with a radius of above 1. • With different values for k1, k2. • With different computation methods for f. Some obvious methods are f computation in the neighborhood of the object (e.g. the same neighborhood that is used for motion) or f computation as the fraction of loading actions of the agent during the last N simulation cycles or as the fraction of occupied places the agent encountered during the last N simulation cycles.
    [Show full text]