INFORMATION AND COMMUNICATION TECHNOLOGIES IMPROVING EFFICIENCIES WAYFINDING SWARM CREATURES EXPLORING THE 3D DYNAMIC VIRTUAL WORLDS
|
|
- Patrick Lynch
- 5 years ago
- Views:
Transcription
1 INFORMATION AND COMMUNICATION TECHNOLOGIES IMPROVING EFFICIENCIES Refereed Paper WAYFINDING SWARM CREATURES EXPLORING THE 3D DYNAMIC VIRTUAL WORLDS University of Sydney, Australia Mary Lou Maher University of Sydney, Australia ABSTRACT The use of 3D virtual worlds in the construction industry is not limited to simulation of building models. Whilst in a 3D virtual world, designers can view their work from various viewpoints by moving around. However explorations within these environments are not simple affairs due to the absence of many sensorial stimuli that exist in the physical world. Hence there needs to be a tool aiding designers to explore the world. We present a wayfinding aid based on a swarm model. The wayfinding creatures produce dynamic trails leading to desired destinations. We describe the swarm rules developed to create such behaviour in wayfinding creatures. Keywords: Virtual Environments, Wayfinding, Swarm Intelligence, Construction Industry Clients Driving Innovation: Moving Ideas into Practice (12-14 March 2006) 1
2 1.0 INTRODUCTION The increased use of Information and Communications Technology (ICT) in the construction industry has enabled a more efficient communication between different stake holders. ICT provides an effective communication channel between main contractors and sub-contractors whilst maintaining information between them using automated information management systems. ICT also allows clients and developers to quickly visualise models being built in 3D, and enables them to see the effects of any alteration. Figure 1 Simulated building Figure 2 Virtual environment as a design tool The use of 3D virtual worlds in the construction industry is not limited to a simulation of building models as shown in Figure 1. These worlds enable designers to be in their designs. Whilst in a 3D virtual world, designers can view their work from various viewpoints. They can also design while they are in the worlds as shown in Figure 2. In order to design, designers first have to arrive at a particular location in the world which gives them a better view of the model being considered. However explorations within these environments are not simple affairs (Darken and Sibert 2001) due to the absence of many sensorial stimuli that exist in the physical world (Chittaro and Burigat 2004). Hence there needs to be a tool aiding designers to explore the world (Darken and Paterson 2001). Our wayfinding aid consists of an interface agent and swarm based path forming creatures. The interface agent acts as a mediator between a user and swarm creatures. It identifies the user s requested target and sends out swarm creatures to locate it. Swarm creatures are sent out into the virtual environment to search for the requested target, and, once found, to return back to the user whilst creating a path. This paper focuses on the use of swarm creatures to generate paths in a dynamically changing environment. 2.0 SWARM INTELLIGENCE Bonabeau et. al. (1999) define swarm intelligence as any attempt to design algorithms or distributed problem-solving devices inspired by the collective behaviour of social insect colonies and other animal societies. As an individual, these insects do not possess enough intelligence to survive. However as a colony of insects they find food and shelter to sustain their existence. Social insects have three traits that make them successful: flexibility, robustness, and self-organisation. A colony is flexible in that it can adapt to the changing environment. It is robust in that even when individuals fail, it can continue performing its tasks. Each individual acts autonomously without intervention from a controlling body. There are many swarm models being used for various purposes (Kennedy and Ebergart 2001). For this research, the ant foraging model of swarm is chosen to be a base swarm model for wayfinding in dynamic virtual environments. Clients Driving Innovation: Moving Ideas into Practice (12-14 March 2006) 2
3 2.1 ANT FORAGING MODEL Wayfinding swarm creatures exploring the 3D dynamic virtual worlds Ants perform complex tasks even though each ant is only governed by simple behavioural rules. For example, harvester ants illustrate this complex behaviour in locating food. They find the shortest path to food sources while prioritising food sources depending on the distance and the ease of access. Different species of ants communicate differently when foraging. Some species communicate directly to each other while others use the environment as their communication medium. We focus on the latter form of communication which is termed stigmergy. Stigmergy allows each ant to modify the environment to communicate to others about the location of a food source when foraging for food. Ants use chemical droppings, called pheromones, to indicate trails between the nest and food sources. Pheromones are chemical compositions which evaporate over time. Consequently a trail once laid will dissipate when it is not reinforced by further pheromone markings. When a trail is used frequently, pheromones will accumulate leading to a higher concentration. The following diagrams illustrate the ant foraging model based on stigmergic communication. Figure 3 Ants start looking for food Figure 4 Food located Figure 5 Return while dropping pheromones Figure 6 The shorter path leads to food faster Figure 7 Ants attracted to a stronger scent of pheromones Figure 8 Following the same path to food Initially the ants randomly search for food (Figures 3). An ant following a shorter path arrives before another ant that followed a longer path (Figure 4), and returns sooner to the nest while dropping pheromones along the trail (Figures 5-6). Ants are attracted to a path with a strong scent of pheromones (Figure 7). Pheromones accumulate on a path well travelled by ants while pheromones on a less travelled path will dissipate (Figure 8). The ant foraging model outlined above can be expressed by a simplified set of rules like Resnick s algorithms (Resnick 1995). Rule 1 shows Resnick s ant foraging behaviour algorithm. Clients Driving Innovation: Moving Ideas into Practice (12-14 March 2006) 3
4 Rule 1 Resnick s ant (Resnick 1995) 1. Looking for food * If pheromone trail is weak then wander * Else move towards increasing concentration 2. Acquiring food * If at food then a. Pick it up b. Turn around c. Start laying pheromone trail 3. Returning to nest * Deposit pheromone * Decrease amount of food available 4. Depositing food * If at nest then a. Deposit food b. Stop laying pheromone trail c. Turn around 5. Repeat forever Resnick's ant, as shown in Rule 1, exhibits a similar pattern of behaviour as real ants. The ants wander around randomly until either food or a pheromone trail is found. These ants will continue to travel until they find food. When they return to the nest with food, they drop it and then go back to where the food was found. 2.2 ADAPTIVE BEHAVIOUR OF ANT FORAGING MODEL The ant foraging model allows the ants to adapt to the changing environment. A path is established (Figure 9) as shown in Figures 3 to 8. On this path, an object is introduced causing the ants to stop (Figure 10). Initially the ants try to move around the object randomly (Figure 11). Once again with the use of pheromones, a shorter path around the object will be more concentrated encouraging more ants to follow that path (Figure 12). Figure 7 Following the shortest path to food Figure 8 Introducing an obstacle on the path Figure 9 Moving around the obstacle Figure 10 A new path formed 3.0 SWARM BASED WAYFINDING AID The ant foraging behaviour model has characteristics which are ideal for a wayfinding application (Yoon and Maher 2005). It not only allows a path to be generated but also allows Clients Driving Innovation: Moving Ideas into Practice (12-14 March 2006) 4
5 the established path to adapt to subsequent changes made in the environment. This is vital as when virtual environments are used to design buildings, objects will be created, deleted, and moved to visually assess the design outcomes. The wayfinding swarm rules are presented in Rules 2-4. These rules define how each individual creature makes the decision about a local move. The rules also define what each creature senses and how it acts. These rules have been successfully implemented and simulated in a 2D environment. The implementation demonstrates that the swarm creatures are able to locate a particular target in a virtual environment. A path is formed between the target and home once the target is located. The generated path then can be adapted to subsequent changes made in the environment. 3.1 WAYFINDING SWARM RULES Rule 2 Overall behaviour rule Rule wayfinding_creature_behavior repeat Explore_World until Target_located Return Home The overall rule for the swarm creatures is shown in Rule 2. Rules 3-4 mention attractants and repellents. Both the attractants and the repellents are electronic pheromones dropped by the swarm creatures as they move about in the world. The attractants are dropped by creatures returning home once they find the target. The wayfinding creatures also drop repellents to mark visited spaces while exploring. Hence when they sense repellents in adjacent locations, they are encouraged to move to unexplored spaces. Because the repellents evaporate, the creatures are encouraged to explore the spaces previously visited in due time. Rule 3 describes how a wayfinding creature explores the world while looking for the target. Until the target is located, every time the creature moves, it checks to see if the required target is located in the adjacent locations. If the target is found, the creature simply moves to the target and creates a teleport gate. Otherwise the creature senses the adjacent locations for traces of pheromones. If attractants are found, it moves to the location with the highest concentration of attractants. If not, it drops a repellent in its current location prior to moving onto an adjacent location that is not occupied by objects, other creatures, and, if repellents exist, repellents of concentration above a certain threshold. Rule 3 Explore world rule Rule Explore_World if Target found in adjacent locations Move to Target Create teleport gate else if attractant found in adjacent locations Move to location with highest concentration of attractant else Drop repellent in current Clients Driving Innovation: Moving Ideas into Practice (12-14 March 2006) 5
6 The wayfinding creatures follow Rule 4 when returning home after locating the target. Prior to relocation, the creature drops an attractant in the current location. It then senses whether home is found in the adjacent locations. If located, the creature moves to it then turns back again to explore the world. Otherwise, the creature moves to an adjacent location closer to home than the current location. This adjacent location must not be occupied by obstacles. If the location is occupied by an obstacle, the creature moves around the obstacle by choosing some other adjacent location taking it closer to home either horizontally or vertically compared to the current location. Rule 4 Return home rule Rule Return_Home Drop attractant in current location Repeat if Home found in adjacent locations Move to Home else Move to empty adjacent location closer to Home until Home 3.2 DYNAMIC 2D ENVIRONMENT SIMULATION The swarm rules have been implemented and simulated in a 2D world to test the validity of the algorithms. The size of the world used in the simulations does not properly reflect the size of a large scale virtual environment for which the creatures are being developed. However, the initial result indicates that the creatures are able to create a trail establishing a path between Target and Home. The simulation also shows that the creatures can adapt as the world changes. This is shown as the trail is changed according to the changes made in the world. The various symbols used in the simulation are identified in Figure 11. Figure 11 Symbol Representation Home and Target are located in the world. The creatures have the knowledge of the location of Home but not of the location of Target. Hence the creatures begin exploring first the immediate vicinity. When the creatures find Target, they return while dropping attractants, shown as grey squares in Figure 12. These attractants emerge as a trail to which the creatures in adjacent locations are attracted. They follow the same trail till they too locate Target. They return Home also depositing attractants, thus strengthening the trail. Other creatures which are not in adjacent locations to the trail are unaffected by it. Figure 12 Formation of a trail Figure 13 Finding a new path Clients Driving Innovation: Moving Ideas into Practice (12-14 March 2006) 6
7 When Target and Home change their locations, initially the creatures continue to follow the old path due to the high concentration of the attractants that have accumulated on it. At the end of the old path, the wayfinding creatures randomly wander around due to the relocation of Target. This is shown in Figure 13 where a large number of creatures are gathered around the old Target location (bottom right hand corner of Figure 13). Unable to locate Target in the vicinity of the old Target location, they eventually move away. The creatures reexplore the world trying to re-locate Target. When it is found, a new trail is created. The old path evaporates in time as new attractants are not deposited on it. The simulation shows that it is possible to develop a wayfinding aid based on a swarm model that can readily adapt to the environment. By having simple creatures exploring the world, the swarm tool generates wayfinding aids which adjust to dynamically changing environments without a controlling body. 4.0 FUTURE WORK Currently, the wayfinding swarm rules are being transferred from a simple 2D virtual environment (Figures 12-13) to a large-scale 3D virtual environment (Figures 1-2). Given the disparity between the environments, a feasibility study needs to be carried out to ascertain whether wayfinding aids in micro-worlds can be scaled up to be of use in macro-worlds of considerable size and complexity. The most challenging part would be enabling swarm creatures to effectively sense adjacent locations in 3D. References Bonabeau, E., Dorigo, M., and Theraulaz, G. Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Chittaro L. and Burigat S. 3D Location-pointing as a Navigation Aid for Virtual Environments, Proceedings of AVI 2004: 7th International Conference on Advanced Visual Interfaces, ACM Press, New York, May 2004, Darken, R. P. and Peterson, B. Spatial Orientation, Wayfinding, and Representation, In K. Stanney (Ed.), Handbook of Virtual Environment Technology, Lawrence Erlbaum Associates, New Jersey, Darken, R. P. and Sibert, J. L. Wayfinding Strategies and Behaviors in Large Virtual Worlds, In Proceedings of CHI 96, ACM Press, New York, 2001, Kennedy, J., and Ebergart, R. C. Swarm Intelligence. Academic Press, Resnick, M. Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds. Cambridge MA, MIT Press, Yoon, J.S. and Maher, M.L. A swarm algorithm for wayfinding in dynamic virtual worlds, Proceedings of ACM Symposium on Virtual Reality Software and Technology 2005 (VRST 05), Naval Postgraduate School, Monterey, USA, 2005, to appear. Clients Driving Innovation: Moving Ideas into Practice (12-14 March 2006) 7
1) Complexity, Emergence & CA (sb) 2) Fractals and L-systems (sb) 3) Multi-agent systems (vg) 4) Swarm intelligence (vg) 5) Artificial evolution (vg)
1) Complexity, Emergence & CA (sb) 2) Fractals and L-systems (sb) 3) Multi-agent systems (vg) 4) Swarm intelligence (vg) 5) Artificial evolution (vg) 6) Virtual Ecosystems & Perspectives (sb) Inspired
More informationbiologically-inspired computing lecture 20 Informatics luis rocha 2015 biologically Inspired computing INDIANA UNIVERSITY
lecture 20 -inspired Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays Lab 0
More informationSWARM INTELLIGENCE. Mario Pavone Department of Mathematics & Computer Science University of Catania
Worker Ant #1: I'm lost! Where's the line? What do I do? Worker Ant #2: Help! Worker Ant #3: We'll be stuck here forever! Mr. Soil: Do not panic, do not panic. We are trained professionals. Now, stay calm.
More informationPSYCO 457 Week 9: Collective Intelligence and Embodiment
PSYCO 457 Week 9: Collective Intelligence and Embodiment Intelligent Collectives Cooperative Transport Robot Embodiment and Stigmergy Robots as Insects Emergence The world is full of examples of intelligence
More informationBiological Inspirations for Distributed Robotics. Dr. Daisy Tang
Biological Inspirations for Distributed Robotics Dr. Daisy Tang Outline Biological inspirations Understand two types of biological parallels Understand key ideas for distributed robotics obtained from
More informationSwarm Intelligence. Corey Fehr Merle Good Shawn Keown Gordon Fedoriw
Swarm Intelligence Corey Fehr Merle Good Shawn Keown Gordon Fedoriw Ants in the Pants! An Overview Real world insect examples Theory of Swarm Intelligence From Insects to Realistic A.I. Algorithms Examples
More informationSITUATED DESIGN OF VIRTUAL WORLDS USING RATIONAL AGENTS
SITUATED DESIGN OF VIRTUAL WORLDS USING RATIONAL AGENTS MARY LOU MAHER AND NING GU Key Centre of Design Computing and Cognition University of Sydney, Australia 2006 Email address: mary@arch.usyd.edu.au
More informationShuffled Complex Evolution
Shuffled Complex Evolution Shuffled Complex Evolution An Evolutionary algorithm That performs local and global search A solution evolves locally through a memetic evolution (Local search) This local search
More informationBiologically-inspired Autonomic Wireless Sensor Networks. Haoliang Wang 12/07/2015
Biologically-inspired Autonomic Wireless Sensor Networks Haoliang Wang 12/07/2015 Wireless Sensor Networks A collection of tiny and relatively cheap sensor nodes Low cost for large scale deployment Limited
More informationNAVIGATION OF MOBILE ROBOT USING THE PSO PARTICLE SWARM OPTIMIZATION
Journal of Academic and Applied Studies (JAAS) Vol. 2(1) Jan 2012, pp. 32-38 Available online @ www.academians.org ISSN1925-931X NAVIGATION OF MOBILE ROBOT USING THE PSO PARTICLE SWARM OPTIMIZATION Sedigheh
More informationADVANCES IN IT FOR BUILDING DESIGN
ADVANCES IN IT FOR BUILDING DESIGN J. S. Gero Key Centre of Design Computing and Cognition, University of Sydney, NSW, 2006, Australia ABSTRACT Computers have been used building design since the 1950s.
More informationSWARM ROBOTICS: PART 2. Dr. Andrew Vardy COMP 4766 / 6912 Department of Computer Science Memorial University of Newfoundland St.
SWARM ROBOTICS: PART 2 Dr. Andrew Vardy COMP 4766 / 6912 Department of Computer Science Memorial University of Newfoundland St. John s, Canada PRINCIPLE: SELF-ORGANIZATION 2 SELF-ORGANIZATION Self-organization
More informationSWARM ROBOTICS: PART 2
SWARM ROBOTICS: PART 2 PRINCIPLE: SELF-ORGANIZATION Dr. Andrew Vardy COMP 4766 / 6912 Department of Computer Science Memorial University of Newfoundland St. John s, Canada 2 SELF-ORGANIZATION SO in Non-Biological
More informationCollective Robotics. Marcin Pilat
Collective Robotics Marcin Pilat Introduction Painting a room Complex behaviors: Perceptions, deductions, motivations, choices Robotics: Past: single robot Future: multiple, simple robots working in teams
More informationTwo Foraging Algorithms for Robot Swarms Using Only Local Communication
Two Foraging Algorithms for Robot Swarms Using Only Local Communication Nicholas R. Hoff III Amelia Sagoff Robert J. Wood and Radhika Nagpal TR-07-10 Computer Science Group Harvard University Cambridge,
More informationSubsumption Architecture in Swarm Robotics. Cuong Nguyen Viet 16/11/2015
Subsumption Architecture in Swarm Robotics Cuong Nguyen Viet 16/11/2015 1 Table of content Motivation Subsumption Architecture Background Architecture decomposition Implementation Swarm robotics Swarm
More informationIn vivo, in silico, in machina: ants and robots balance memory and communication to collectively exploit information
In vivo, in silico, in machina: ants and robots balance memory and communication to collectively exploit information Melanie E. Moses, Kenneth Letendre, Joshua P. Hecker, Tatiana P. Flanagan Department
More informationSwarm Robotics. Lecturer: Roderich Gross
Swarm Robotics Lecturer: Roderich Gross 1 Outline Why swarm robotics? Example domains: Coordinated exploration Transportation and clustering Reconfigurable robots Summary Stigmergy revisited 2 Sources
More informationInformation Quality in Critical Infrastructures. Andrea Bondavalli.
Information Quality in Critical Infrastructures Andrea Bondavalli andrea.bondavalli@unifi.it Department of Matematics and Informatics, University of Florence Firenze, Italy Hungarian Future Internet -
More informationAn Introduction to Swarm Intelligence Issues
An Introduction to Swarm Intelligence Issues Gianni Di Caro gianni@idsia.ch IDSIA, USI/SUPSI, Lugano (CH) 1 Topics that will be discussed Basic ideas behind the notion of Swarm Intelligence The role of
More informationONE of the many fascinating phenomena
1 Stigmergic navigation on an RFID floor with a multi-robot team Ali Abdul Khaliq, Maurizio Di Rocco, Alessandro Saffiotti, Abstract Stigmergy is a mechanism that allows the coordination between agents
More informationKOVAN Dept. of Computer Eng. Middle East Technical University Ankara, Turkey
Swarm Robotics: From sources of inspiration to domains of application Erol Sahin KOVAN Dept. of Computer Eng. Middle East Technical University Ankara, Turkey http://www.kovan.ceng.metu.edu.tr What is Swarm
More informationInteractive Surface for Bio-inspired Robotics, Re-examining Foraging Models
Interactive Surface for Bio-inspired Robotics, Re-examining Foraging Models Olivier Simonin, Thomas Huraux, François Charpillet Université Henri Poincaré and INRIA Nancy Grand Est MAIA team, LORIA Laboratory
More informationDeveloping Frogger Player Intelligence Using NEAT and a Score Driven Fitness Function
Developing Frogger Player Intelligence Using NEAT and a Score Driven Fitness Function Davis Ancona and Jake Weiner Abstract In this report, we examine the plausibility of implementing a NEAT-based solution
More informationDynamic Designs of 3D Virtual Worlds Using Generative Design Agents
Dynamic Designs of 3D Virtual Worlds Using Generative Design Agents GU Ning and MAHER Mary Lou Key Centre of Design Computing and Cognition, University of Sydney Keywords: Abstract: Virtual Environments,
More informationMASON. A Java Multi-agent Simulation Library. Sean Luke Gabriel Catalin Balan Liviu Panait Claudio Cioffi-Revilla Sean Paus
MASON A Java Multi-agent Simulation Library Sean Luke Gabriel Catalin Balan Liviu Panait Claudio Cioffi-Revilla Sean Paus George Mason University s Center for Social Complexity and Department of Computer
More informationTRAFFIC SIGNAL CONTROL WITH ANT COLONY OPTIMIZATION. A Thesis presented to the Faculty of California Polytechnic State University, San Luis Obispo
TRAFFIC SIGNAL CONTROL WITH ANT COLONY OPTIMIZATION A Thesis presented to the Faculty of California Polytechnic State University, San Luis Obispo In Partial Fulfillment of the Requirements for the Degree
More informationSwarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks
Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks Floriano De Rango 1, Nunzia Palmieri 1, Xin-She Yang 2, Salvatore Marano 1 arxiv:1804.08096v1
More informationTowards an Engineering Science of Robot Foraging
Towards an Engineering Science of Robot Foraging Alan FT Winfield Abstract Foraging is a benchmark problem in robotics - especially for distributed autonomous robotic systems. The systematic study of robot
More informationSorting in Swarm Robots Using Communication-Based Cluster Size Estimation
Sorting in Swarm Robots Using Communication-Based Cluster Size Estimation Hongli Ding and Heiko Hamann Department of Computer Science, University of Paderborn, Paderborn, Germany hongli.ding@uni-paderborn.de,
More informationRobotic Systems ECE 401RB Fall 2007
The following notes are from: Robotic Systems ECE 401RB Fall 2007 Lecture 14: Cooperation among Multiple Robots Part 2 Chapter 12, George A. Bekey, Autonomous Robots: From Biological Inspiration to Implementation
More informationPaulo Urbano. LabMag Universidade de Lisboa
Multi-Agent t Coordination and Collective Artificial Paintings Paulo Urbano LabMag Universidade de Lisboa pub@di.fc.ul.pt My Goal: Apply techniques for coordinating a group of agents to Swarm Art I m Going
More informationSelf-Organised Task Allocation in a Group of Robots
Self-Organised Task Allocation in a Group of Robots Thomas H. Labella, Marco Dorigo and Jean-Louis Deneubourg Technical Report No. TR/IRIDIA/2004-6 November 30, 2004 Published in R. Alami, editor, Proceedings
More informationMaze Solving Algorithms for Micro Mouse
Maze Solving Algorithms for Micro Mouse Surojit Guha Sonender Kumar surojitguha1989@gmail.com sonenderkumar@gmail.com Abstract The problem of micro-mouse is 30 years old but its importance in the field
More informationSequential Task Execution in a Minimalist Distributed Robotic System
Sequential Task Execution in a Minimalist Distributed Robotic System Chris Jones Maja J. Matarić Computer Science Department University of Southern California 941 West 37th Place, Mailcode 0781 Los Angeles,
More informationA New Kind of Art [Based on Autonomous Collective Robotics]
25 A New Kind of Art [Based on Autonomous Collective Robotics] Leonel Moura and Henrique Garcia Pereira Introduction We started working with robots as art performers around the turn of the century. Other
More informationCollaborative Foraging using Beacons
Collaborative Foraging using Beacons Brian Hrolenok, Sean Luke, Keith Sullivan, and Christopher Vo Department of Computer Science, George Mason University MSN 4A5, Fairfax, VA 223, USA {bhroleno, sean,
More informationReview of Soft Computing Techniques used in Robotics Application
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 3 (2013), pp. 101-106 International Research Publications House http://www. irphouse.com /ijict.htm Review
More informationCS 599: Distributed Intelligence in Robotics
CS 599: Distributed Intelligence in Robotics Winter 2016 www.cpp.edu/~ftang/courses/cs599-di/ Dr. Daisy Tang All lecture notes are adapted from Dr. Lynne Parker s lecture notes on Distributed Intelligence
More informationA Genetic Algorithm-Based Controller for Decentralized Multi-Agent Robotic Systems
A Genetic Algorithm-Based Controller for Decentralized Multi-Agent Robotic Systems Arvin Agah Bio-Robotics Division Mechanical Engineering Laboratory, AIST-MITI 1-2 Namiki, Tsukuba 305, JAPAN agah@melcy.mel.go.jp
More informationCS594, Section 30682:
CS594, Section 30682: Distributed Intelligence in Autonomous Robotics Spring 2003 Tuesday/Thursday 11:10 12:25 http://www.cs.utk.edu/~parker/courses/cs594-spring03 Instructor: Dr. Lynne E. Parker ½ TA:
More informationCooperative navigation in robotic swarms
1 Cooperative navigation in robotic swarms Frederick Ducatelle, Gianni A. Di Caro, Alexander Förster, Michael Bonani, Marco Dorigo, Stéphane Magnenat, Francesco Mondada, Rehan O Grady, Carlo Pinciroli,
More informationHOW CAN CAAD TOOLS BE MORE USEFUL AT THE EARLY STAGES OF DESIGNING?
HOW CAN CAAD TOOLS BE MORE USEFUL AT THE EARLY STAGES OF DESIGNING? Towards Situated Agents That Interpret JOHN S GERO Krasnow Institute for Advanced Study, USA and UTS, Australia john@johngero.com AND
More informationFrom Tom Thumb to the Dockers: Some Experiments with Foraging Robots
From Tom Thumb to the Dockers: Some Experiments with Foraging Robots Alexis Drogoul, Jacques Ferber LAFORIA, Boîte 169,Université Paris VI, 75252 PARIS CEDEX O5 FRANCE drogoul@laforia.ibp.fr, ferber@laforia.ibp.fr
More informationAdaptive Control in Swarm Robotic Systems
The Hilltop Review Volume 3 Issue 1 Fall Article 7 October 2009 Adaptive Control in Swarm Robotic Systems Hanyi Dai Western Michigan University Follow this and additional works at: http://scholarworks.wmich.edu/hilltopreview
More informationLaps to Criterion 160. Pheromone Duration (min)
Experiments in Path Optimization via Pheromone Trails by Simulated Robots Jason L. Almeter y September 17, 1996 Abstract Ants lay pheromone trails to lead other individuals to a destination. Due to stochastic
More informationCSCI 445 Laurent Itti. Group Robotics. Introduction to Robotics L. Itti & M. J. Mataric 1
Introduction to Robotics CSCI 445 Laurent Itti Group Robotics Introduction to Robotics L. Itti & M. J. Mataric 1 Today s Lecture Outline Defining group behavior Why group behavior is useful Why group behavior
More informationSwarm Robotics. Clustering and Sorting
Swarm Robotics Clustering and Sorting By Andrew Vardy Associate Professor Computer Science / Engineering Memorial University of Newfoundland St. John s, Canada Deneubourg JL, Goss S, Franks N, Sendova-Franks
More informationContact information. Tony White, Associate Professor
Contact information Tony White, Associate Professor Office: Hertzberg 5354 Tel: 520-2600 x2208 Fax: 520-4334 E-mail: arpwhite@scs.carleton.ca E-mail: arpwhite@hotmail.com Web: http://www.scs.carleton.ca/~arpwhite
More informationstart carrying resource? >Ps since last crumb? reached goal? reached home? announce private crumbs clear private crumb list
Blazing a trail: Insect-inspired resource transportation by a robot team Richard T. Vaughan, Kasper Stfiy, Gaurav S. Sukhatme, and Maja J. Matarić Robotics Research Laboratories, University of Southern
More informationExpert Assessment of Stigmergy: A Report for the Department of National Defence
Expert Assessment of Stigmergy: A Report for the Department of National Defence Contract No. File No. Client Reference No.: W7714-040899/003/SV 011 sv.w7714-040899 W7714-4-0899 Requisition No. W7714-040899
More informationImportant Tools and Perspectives for the Future of AI
Important Tools and Perspectives for the Future of AI The Norwegian University of Science and Technology (NTNU) Trondheim, Norway keithd@idi.ntnu.no April 1, 2011 Outline 1 Artificial Life 2 Cognitive
More informationFormica ex Machina: Ant Swarm Foraging from Physical to Virtual and Back Again
Formica ex Machina: Ant Swarm Foraging from Physical to Virtual and Back Again Joshua P. Hecker 1, Kenneth Letendre 1,2, Karl Stolleis 1, Daniel Washington 1, and Melanie E. Moses 1,2 1 Department of Computer
More informationAutonomous Self-deployment of Wireless Access Networks in an Airport Environment *
Autonomous Self-deployment of Wireless Access Networks in an Airport Environment * Holger Claussen Bell Labs Research, Swindon, UK. * This work was part-supported by the EU Commission through the IST FP5
More informationSWARM-BOT: A Swarm of Autonomous Mobile Robots with Self-Assembling Capabilities
SWARM-BOT: A Swarm of Autonomous Mobile Robots with Self-Assembling Capabilities Francesco Mondada 1, Giovanni C. Pettinaro 2, Ivo Kwee 2, André Guignard 1, Luca Gambardella 2, Dario Floreano 1, Stefano
More informationBehaviour-Based Control. IAR Lecture 5 Barbara Webb
Behaviour-Based Control IAR Lecture 5 Barbara Webb Traditional sense-plan-act approach suggests a vertical (serial) task decomposition Sensors Actuators perception modelling planning task execution motor
More informationA Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures
A Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures D.M. Rojas Castro, A. Revel and M. Ménard * Laboratory of Informatics, Image and Interaction (L3I)
More informationMulti-Robot Coordination. Chapter 11
Multi-Robot Coordination Chapter 11 Objectives To understand some of the problems being studied with multiple robots To understand the challenges involved with coordinating robots To investigate a simple
More informationCognitive robots and emotional intelligence Cloud robotics Ethical, legal and social issues of robotic Construction robots Human activities in many
Preface The jubilee 25th International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2016 was held in the conference centre of the Best Western Hotel M, Belgrade, Serbia, from 30 June to 2 July
More informationAN AUTONOMOUS SIMULATION BASED SYSTEM FOR ROBOTIC SERVICES IN PARTIALLY KNOWN ENVIRONMENTS
AN AUTONOMOUS SIMULATION BASED SYSTEM FOR ROBOTIC SERVICES IN PARTIALLY KNOWN ENVIRONMENTS Eva Cipi, PhD in Computer Engineering University of Vlora, Albania Abstract This paper is focused on presenting
More informationOptimization of Tile Sets for DNA Self- Assembly
Optimization of Tile Sets for DNA Self- Assembly Joel Gawarecki Department of Computer Science Simpson College Indianola, IA 50125 joel.gawarecki@my.simpson.edu Adam Smith Department of Computer Science
More informationDipartimento di Elettronica Informazione e Bioingegneria Robotics
Dipartimento di Elettronica Informazione e Bioingegneria Robotics Behavioral robotics @ 2014 Behaviorism behave is what organisms do Behaviorism is built on this assumption, and its goal is to promote
More informationCapturing and Adapting Traces for Character Control in Computer Role Playing Games
Capturing and Adapting Traces for Character Control in Computer Role Playing Games Jonathan Rubin and Ashwin Ram Palo Alto Research Center 3333 Coyote Hill Road, Palo Alto, CA 94304 USA Jonathan.Rubin@parc.com,
More informationInvestigation of Navigating Mobile Agents in Simulation Environments
Investigation of Navigating Mobile Agents in Simulation Environments Theses of the Doctoral Dissertation Richárd Szabó Department of Software Technology and Methodology Faculty of Informatics Loránd Eötvös
More informationFuzzy-Heuristic Robot Navigation in a Simulated Environment
Fuzzy-Heuristic Robot Navigation in a Simulated Environment S. K. Deshpande, M. Blumenstein and B. Verma School of Information Technology, Griffith University-Gold Coast, PMB 50, GCMC, Bundall, QLD 9726,
More informationDesigning Toys That Come Alive: Curious Robots for Creative Play
Designing Toys That Come Alive: Curious Robots for Creative Play Kathryn Merrick School of Information Technologies and Electrical Engineering University of New South Wales, Australian Defence Force Academy
More informationParadigms, Models and Technologies for Building and Simulating Self-Organising Systems
Paradigms, Models and Technologies for Building and Simulating Ing. Luca Gardelli DEIS - Department of Electronics, Computer Science & Systems ALMA MATER STUDIORUM Università di Bologna Via Venezia 52,
More informationTraffic Control for a Swarm of Robots: Avoiding Group Conflicts
Traffic Control for a Swarm of Robots: Avoiding Group Conflicts Leandro Soriano Marcolino and Luiz Chaimowicz Abstract A very common problem in the navigation of robotic swarms is when groups of robots
More informationMathematical Analysis of 2048, The Game
Advances in Applied Mathematical Analysis ISSN 0973-5313 Volume 12, Number 1 (2017), pp. 1-7 Research India Publications http://www.ripublication.com Mathematical Analysis of 2048, The Game Bhargavi Goel
More informationAlice in Pheromone Land: An Experimental Setup for the Study of Ant-like Robots
Alice in Pheromone Land: An Experimental Setup for the Study of Ant-like Robots Simon Garnier a, Fabien Tâche b, Maud Combe a, Anne Grimal a and Guy Theraulaz a a Centre de Recherches sur la Cognition
More informationA STUDY OF WAYFINDING IN TAIPEI METRO STATION TRANSFER: MULTI-AGENT SIMULATION APPROACH
A STUDY OF WAYFINDING IN TAIPEI METRO STATION TRANSFER: MULTI-AGENT SIMULATION APPROACH Kuo-Chung WEN 1 * and Wei-Chen SHEN 2 1 Associate Professor, Graduate Institute of Architecture and Urban Design,
More informationSwarm Development Tools. Ricardo Hoar
Swarm Development Tools Ricardo Hoar Swarms Emergent global behaviour from many parallel local interactions Relatively simple local rules can produce complex results Since this idea can be applied to many
More informationFROM LOCAL ACTIONS TO GLOBAL TASKS: STIGMERGY AND COLLECTIVE ROBOTICS
FROM LOCAL ACTIONS TO GLOBAL TASKS: STIGMERGY AND COLLECTIVE ROBOTICS R. Beckers 1,2, O.E. Holland 1,3 and J.L. Deneubourg 2 1 ZiF-Universität Bielefeld, Wellenberg 1, D-33615 Bielefeld 2 Centre for non-linear
More informationComparison of Haptic and Non-Speech Audio Feedback
Comparison of Haptic and Non-Speech Audio Feedback Cagatay Goncu 1 and Kim Marriott 1 Monash University, Mebourne, Australia, cagatay.goncu@monash.edu, kim.marriott@monash.edu Abstract. We report a usability
More information1 Swarms A long time ago, people discovered the variety of the interesting insect or animal behaviors in the nature. A ock of birds sweeps across the
Swarm Intelligence: Literature Overview Yang Liu and Kevin M. Passino Dept. of Electrical Engineering The Ohio State University 2015 Neil Ave. Columbus, OH 43210 Tel: (614)292-5716, fax: (614)292-7596
More informationA Modified Ant Colony Optimization Algorithm for Implementation on Multi-Core Robots
A Modified Ant Colony Optimization Algorithm for Implementation on Multi-Core Robots Timothy Krentz Chase Greenhagen Aaron Roggow Danielle Desmond Sami Khorbotly Department of Electrical and Computer Engineering
More informationMULTI-LAYERED HYBRID ARCHITECTURE TO SOLVE COMPLEX TASKS OF AN AUTONOMOUS MOBILE ROBOT
MULTI-LAYERED HYBRID ARCHITECTURE TO SOLVE COMPLEX TASKS OF AN AUTONOMOUS MOBILE ROBOT F. TIECHE, C. FACCHINETTI and H. HUGLI Institute of Microtechnology, University of Neuchâtel, Rue de Tivoli 28, CH-2003
More informationBachelor thesis. Influence map based Ms. Pac-Man and Ghost Controller. Johan Svensson. Abstract
2012-07-02 BTH-Blekinge Institute of Technology Uppsats inlämnad som del av examination i DV1446 Kandidatarbete i datavetenskap. Bachelor thesis Influence map based Ms. Pac-Man and Ghost Controller Johan
More informationThe Behavior Evolving Model and Application of Virtual Robots
The Behavior Evolving Model and Application of Virtual Robots Suchul Hwang Kyungdal Cho V. Scott Gordon Inha Tech. College Inha Tech College CSUS, Sacramento 253 Yonghyundong Namku 253 Yonghyundong Namku
More informationCHAPTER 5 PSO AND ACO BASED PID CONTROLLER
128 CHAPTER 5 PSO AND ACO BASED PID CONTROLLER 5.1 INTRODUCTION The quality and stability of the power supply are the important factors for the generating system. To optimize the performance of electrical
More informationInstructors: Prof. Takashi Hiyama (TH) Prof. Hassan Bevrani (HB) Syafaruddin, D.Eng (S) Time: Wednesday,
Intelligent System Application to Power System Instructors: Prof. Takashi Hiyama (TH) Prof. Hassan Bevrani (HB) Syafaruddin, D.Eng (S) Time: Wednesday, 10.20-11.50 Venue: Room 208 Intelligent System Application
More informationCYCLIC GENETIC ALGORITHMS FOR EVOLVING MULTI-LOOP CONTROL PROGRAMS
CYCLIC GENETIC ALGORITHMS FOR EVOLVING MULTI-LOOP CONTROL PROGRAMS GARY B. PARKER, CONNECTICUT COLLEGE, USA, parker@conncoll.edu IVO I. PARASHKEVOV, CONNECTICUT COLLEGE, USA, iipar@conncoll.edu H. JOSEPH
More informationDESIGN AGENTS IN VIRTUAL WORLDS. A User-centred Virtual Architecture Agent. 1. Introduction
DESIGN GENTS IN VIRTUL WORLDS User-centred Virtual rchitecture gent MRY LOU MHER, NING GU Key Centre of Design Computing and Cognition Department of rchitectural and Design Science University of Sydney,
More informationVI51 Project Subjects
VI51 Project Subjects Projet Project's groups must be composed by 3 or 4 students Evaluation critera : o Final presentation of the project (10 minutes) o Analysis and Design Report (20 pages) o Project
More informationImplicit Fitness Functions for Evolving a Drawing Robot
Implicit Fitness Functions for Evolving a Drawing Robot Jon Bird, Phil Husbands, Martin Perris, Bill Bigge and Paul Brown Centre for Computational Neuroscience and Robotics University of Sussex, Brighton,
More informationProbabilistic Modelling of a Bio-Inspired Collective Experiment with Real Robots
Probabilistic Modelling of a Bio-Inspired Collective Experiment with Real Robots A. Martinoli, and F. Mondada Microcomputing Laboratory, Swiss Federal Institute of Technology IN-F Ecublens, CH- Lausanne
More informationElectric Vehicle Urban Exploration by Anti-pheromone Swarm based Algorithms
Electric Vehicle Urban Exploration by Anti-pheromone Swarm based Algorithms 1 Rubén Martín García, 1,2 Francisco Prieto-Castrillo, 1 Gabriel Villarrubia González and 1 Javier Bajo University of Salamanca,
More informationMobile ACORoute: Route Recommendation Based on Communication by Pheromones
Mobile ACORoute: Route Recommendation Based on Communication by Pheromones Carla S. G. Pires, Marilton S. de Aguiar, and Paulo R. Ferreira Centro de Desenvolvimento Tecnológico, Universidade Federal de
More informationImprovement of Robot Path Planning Using Particle. Swarm Optimization in Dynamic Environments. with Mobile Obstacles and Target
Advanced Studies in Biology, Vol. 3, 2011, no. 1, 43-53 Improvement of Robot Path Planning Using Particle Swarm Optimization in Dynamic Environments with Mobile Obstacles and Target Maryam Yarmohamadi
More informationChapter 5: Game Analytics
Lecture Notes for Managing and Mining Multiplayer Online Games Summer Semester 2017 Chapter 5: Game Analytics Lecture Notes 2012 Matthias Schubert http://www.dbs.ifi.lmu.de/cms/vo_managing_massive_multiplayer_online_games
More informationA Study on the Navigation System for User s Effective Spatial Cognition
A Study on the Navigation System for User s Effective Spatial Cognition - With Emphasis on development and evaluation of the 3D Panoramic Navigation System- Seung-Hyun Han*, Chang-Young Lim** *Depart of
More informationMulti-Agent Programming Contest Scenario Description 2009 Edition
Multi-Agent Programming Contest Scenario Description 2009 Edition Revised 18.06.2009 http://www.multiagentcontest.org/2009 Tristan Behrens Mehdi Dastani Jürgen Dix Michael Köster Peter Novák An unknown
More informationMultiagent systems: Lessons from social insects and collective
Multiagent systems: Lessons from social insects and collective robotics O.E.Holland Intelligent Autonomous Systems Laboratory Faculty of Engineering [Jniversity of the West of England Bristol BS16 1QY
More informationThe Gender Factor in Virtual Reality Navigation and Wayfinding
The Gender Factor in Virtual Reality Navigation and Wayfinding Joaquin Vila, Ph.D. Applied Computer Science Illinois State University javila@.ilstu.edu Barbara Beccue, Ph.D. Applied Computer Science Illinois
More information2012 Alabama Robotics Competition Challenge Descriptions
2012 Alabama Robotics Competition Challenge Descriptions General Introduction The following pages provide a description of each event and an overview of how points are scored for each event. The overall
More informationMazeBot. Our Urban City. Challenge Manual
MazeBot Our Urban City Challenge Manual Updated as of 27 th February 2017 Eligibility Participants must be between the ages of 7 and 12 (inclusive) as of 31 December 2017. The minimum number of participants
More informationRequirements Specification
Requirements Specification Software Engineering Group 6 12/3/2012: Requirements Specification, v1.0 March 2012 - Second Deliverable Contents: Page no: Introduction...3 Customer Requirements...3 Use Cases...4
More informationSwarming the Kingdom: A New Multiagent Systems Approach to N-Queens
Swarming the Kingdom: A New Multiagent Systems Approach to N-Queens Alex Kutsenok 1, Victor Kutsenok 2 Department of Computer Science and Engineering 1, Michigan State University, East Lansing, MI 48825
More informationCreative Design. Sarah Fdili Alaoui
Creative Design Sarah Fdili Alaoui saralaoui@lri.fr Outline A little bit about me A little bit about you What will this course be about? Organisation Deliverables Communication Readings Who are you? Presentation
More informationEmbodiment of Honeybee s Thermotaxis in a Mobile Robot Swarm
Embodiment of Honeybee s Thermotaxis in a Mobile Robot Swarm Daniela Kengyel 1, Thomas Schmickl 2, Heiko Hamann 2, Ronald Thenius 2, and Karl Crailsheim 2 1 University of Applied Sciences St. Poelten,
More information