Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Related Work
The Cleaning Protocol
Cleaning Protocol -Definitions and Requirements
The Clean Cleaning Protocol
Analysis
Detailed Analysis
Experimental Results
Discussion and Conclusion
References
All Topics
Computer Science
Artificial Intelligence

Cooperative Cleaners: A Study In Ant Robotics

Profile image of Yaniv AltshulerYaniv Altshuler

2008, … International Journal of …

Abstract

In the world of living creatures, ``simple minded'' animals often cooperate to achieve common goals with amazing performance. One can consider this idea in the context of robotics, and suggest models for programming goal-oriented behavior into the members of a group of simple robots lacking global supervision. This can be done by controlling the local interactions between the robot agents, to have them jointly carry out a given mission. As a test case we analyze the problem of many simple robots cooperating to clean the dirty floor of a non-convex region in $\mbox{\bf Z}^{2}$, using the dirt on the floor as the main means of inter-robot communication.

Related papers

Multi-agent Cooperative Cleaning of Expanding Domains
Yaniv Altshuler

Several recent works considered multi-a(ge)nt robotics in static environments. In this work we examine ways of operating in dynamic environments, where changes take place independently of the agents' activity. The work focuses on a dynamic variant of the \emph{Cooperative Cleaners} problem, a problem that requires several simple agents to clean a connected region of ``dirty'' pixels in $\mbox{\bf Z}^{2}$. A number of simple agents move in this dirty region, each having the ability to ``clean'' the place it is located in. Their goal is to jointly clean the given dirty region. The dynamic variant of the problem involves a deterministic expansion of dirt in the environment, simulating spreading of \emph{contamination}, or \emph{fire}. Theoretical lower bounds for the problem are presented, as well as various impossibility results. A cleaning protocol for the problem is presented, and a wealth of experimental results testing its performance in comparison to the lower bounds. Several analytic upper bounds for the proposed protocol are also presented, accompanied with appropriate experimental results.

View PDFchevron_right
The Cooperative Cleaners Problem in Stochastic Dynamic Environments
Yaniv Altshuler

2012

Abstract: In this paper we study the strengths and limitations of collaborative teams of simple agents. In particular, we discuss the efficient use of" ant robots" for covering a connected region on the $ Z^{2} $ grid, whose area is unknown in advance and which expands stochastically. Specifically, we discuss the problem where an initial connected region of $ S_0 $ boundary tiles expand outward with probability $ p $ at every time step.

View PDFchevron_right
From Ants to Robots: A Decentalised Task Allocation Model for A Swarm of Robots
Sifat Momen
View PDFchevron_right
Counter-ant algorithm for evolving multirobot collaboration
Ilhem Kallel

2008

The use of multirobot systems, is affecting our society in a fundamental way; from their use in hazardous environments, to their application in automated environmental cleanup. In an unknown environment, one of the most important problem related to multirobot systems, is to decide how to coordinate actions in order to achieve tasks in an optimal way. Ant algorithms are proved to be very useful in solving such distributed control problems. We introduce in this paper a modified version of the known ant algorithm, called Counter-Ant Algorithm (CAA). Indeed, the robots' collaborative behaviour is based on repulsion instead of attraction to pheromone, which is a chemical matter open to evaporation and representing the core of ants' cooperation. In order to test the performance of our CAA, we implement, simulate and test our algorithm in a generic multirobot environment. In practical terms, the subdivision of the cleaning space is achieved in emergent and evolving way. A series of simulations show the usefulness of our algorithm for adaptive and cooperative cleanup.

View PDFchevron_right
Division of labor in a group of robots inspired by ants' foraging behavior
Marco Dorigo

2006

In this paper, we analyse the behaviour of a group of robots involved in an object retrieval task. The robots' control system is inspired by a model of ants' foraging. This model emphasises the role of learning in the individual. Individuals adapt to the environment using only locally available information. We show that a simple parameter adaptation is an effective way to improve the efficiency of the group, and that it brings forth division of labour between the members of the group.

View PDFchevron_right
Cooperative escape in ants and robots
jordan kennedy

2021

The solution of complex problems by the collective action of simple agents in both biologically evolved and synthetically engineered systems involves cooperative action. Understanding the resulting emergent solutions requires integrating across the organismal behaviors of many individuals. Here we investigate an ecologically relevant collective task in black carpenter ants Camponotus pennsylvanicus: escape from a soft, erodible confining corral. Individual ants show a transition from individual exploratory excavation at random locations to spatially localized collective exploitative excavation and escape from the corral. A minimal continuum theory that coarse-grains over individual actions and considers their integrated influence on the environment leads to the emergence of an effective phase space of behaviors in terms of excavation strength and cooperation intensity. To test the theory over the range of predicted behaviors, we used custom-built robots (RAnts) that respond to stimu...

View PDFchevron_right
The dynamics of collective sorting robot-like ants and ant-like robots
Claire Detrain

Proceedings of the first …, 1991

A distribured sorting algorithm, inspired by how ant colonies sort their brood, is presenæd for use by robot æams. The robots move randomly, do not communicate, have no hierarchical organisation, have no global representation, can only perceive objects just in front of ...

View PDFchevron_right
The Swarm-bot Experience: Strength and Mobility through Physical Cooperation
Marco Dorigo

In this chapter, we study a collective robotic system that is inspired by the behavior of social insects. The system consists of mobile robots that can autonomously perceive and modify their environment. We investigate the problem of controlling a number of these robots in cooperation based tasks that are too difficult for the robots to solve when operating alone.

View PDFchevron_right
The cooperation of swarm-bots
Jean-louis Deneubourg

IEEE Robotics and …, 2005

View PDFchevron_right
Multi-Agent Autonomous Cleaning
Swaneet Rao, IJRASET Publication

IJRASET, 2021

In today's world, robots are taking over the world by doing the tasks which used to be done by humans a while ago. Robots are continuously evolving into better and more efficient autonomous agents, makes substantial growth in fields like adaptive artificial intelligence. Our main objective of this people is to create an efficient multi agent autonomous environment for robots for cleaning purposes.

View PDFchevron_right

References (76)

  1. I.A. Wagner, A.M. Bruckstein: "Cooperative Cleaners: A Case of Distributed Ant-Robotics", "Communications, Computation, Control, and Signal Processing: A Tribute to Thomas Kailath", pp. 289-308, Kluwer Academic Publishers, The Netherlands, (1997)
  2. M.P. Do-Carmo: "Differential Geometry of Curves and Surfaces", Prentice-Hall, New- Jersey, (1976)
  3. R.A. Brooks: "Elephants Don't Play Chess", Designing Autonomous Agents, P. Maes (ed.), pp. 3-15, MIT press / Elsevier, (1990)
  4. S.Levi: "Artificial Life -the Quest for a New Creation", Penguin, (1992)
  5. S.Sen, M. Sekaran, J. Hale: "Learning to Coordinate Without Sharing Information", Pro- ceedings of AAAI-94, pp. 426-431, (1994)
  6. L.Steels: "Cooperation Between Distributed Agents Through Self-Organization", Decentral- ized A.I -Proc. first European Workshop on Modeling Autonomous Agents in Multi-Agents world, Y.DeMazeau, J.P.Muller (Eds.), pp. 175-196, Elsevier, (1990)
  7. G.Beni, J.Wang: "Theoretical Problems for the Realization of Distributed Robotic Systems", Proc. of 1991 IEEE Internal Conference on Robotics and Automation, pp. 1914-1920, Sacra- mento, CA, April (1991)
  8. V.Breitenberg: Vehicles, MIT Press (1984)
  9. D.Henrich: "Space Efficient Region Filling in Raster Graphics", The Visual Computer, pp. 10:205-215, Springer-Verlag, (1994)
  10. R. Baeza-yates, R. Schott: "Parallel Searching in the Plane", Computational Geometry 5 pp. 143-154 (1995)
  11. I A. Wagner, A.M. Bruckstein, "Row Straightening via Local Interactions" Technical report CIS-9406, Center for Intelligent Systems, Technion, Haifa, May 1994. Accepted for publica- tion in Circuits, Systems, and Signal Processing.
  12. A.M. Bruckstein, "Why the Ant Trails Look So Straight and Nice ," The Mathematical In- telligencer, vol. 15, No. 2, pp. 59-62, 1993.
  13. A.M. Bruckstein, C.L. Mallows and I.A. Wagner, "Probabilistic Pursuits on the Integer Grid," Technical report CIS-9411, Center for Intelligent Systems, Technion, Haifa, September 1994. Accepted for publication in the American Mathematical Monthly.
  14. S. Hedberg, "Robots Cleaning Up Hazardous Waste", AI Expert , May 1995, pp. 20-24, Springer-Verlag.
  15. W. Neubauer, "Locomotion with Articulated Legs in Pipes or Ducts", Robotics and Au- tonomous Systems, 11:163-169. Elseveier 1993.
  16. A. Itai, C.H. Papadimitriou, J.L. Szwarefiter, "Hamilton Paths in Grid Graphs", SIAM J. on Computing, 11:676-686, 1982.
  17. Y. Shoham, M. Tennenholtz, "On Social Laws for Artificial Agent Societies: Off Line De- sign", , v.73 AI-Journal, n.1-2, p.231-252, Feb. 1995.
  18. H.A. Simon, The Sciences of the Artificial, 2'nd ed., MIT Press, 1981.
  19. M. Held, On the Computational Geometry of Pocket Machining, Lecture Notes in Computer Science,Springer-Verlag 1991.
  20. R.A. Russell, "Mobile Robot Guidance Using a Short-Lived Heat Trail," Robotica , Vol. 11, 1993, pp. 427-431.
  21. I.A. Wagner, M. Lindenbaum, A.M. Bruckstein: "Efficiently Searching a Graph by a Smell- Oriented Vertex Process", Annals of Mathematics and Artificial Intelligence, Issue 24, pp. 211-223, (1998)
  22. R.C. Arkin, T. Balch: "Cooperative Multi Agent Robotic Systems", Artificial Intelligence and Mobile Robots, MIT/AAAI Press, Cambridge, MA, (1998)
  23. I.A. Wagner, A.M. Bruckstein: "From Ants to A(ge)nts: A Special Issue on Ant-Robotics", Annals of Mathematics and Artificial Intelligence, Special Issue on Ant Robotics, Kluer Academic Publishers, vol. 31, Nos. 1-4, pp. 1-6, (2001)
  24. B.P.Gerkey, M.J.Mataric: "Sold! Market Methods for Multi-Robot Control", IEEE Transac- tions on Robotics and Automation, Special Issue on Multi-robot Systems, (2002).
  25. M.Golfarelli, D.Maio, S. Rizzi: "A Task-Swap Negotiation Protocol Based on the Contract Net Paradigm", Technical Report, 005-97, CSITE (Research Center For Informatics And Telecommunication Systems, associated with the University of Bologna, Italy), (1997).
  26. G.Rabideau, T.Estlin, T.Chien, A.Barrett: "A Comparison of Coordinated Planning Methods for Cooperating Rovers", Proceedings of the American Institute of Aeronautics and Astro- nautics (AIAA) Space Technology Conference, (1999).
  27. R.Smith: "The Contract Net Protocol: High-Level Communication and Control in a Dis- tributed Problem Solver", IEEE Transactions on Computers C-29 (12), (1980).
  28. S.M.Thayer, M.B.Dias, B.L.Digney, A.Stentz, B.Nabbe, M.Hebert: "Distributed Robotic Mapping of Extreme Environments", Proceedings of SPIE, Vol. 4195, Mobile Robots XV and Telemanipulator and Telepresence Technologies VII, (2000).
  29. M.P.Wellman, P.R.Wurman: "Market-Aware Agents for a Multiagent World", Robotics and Autonomous Systems, Vol. 24, pp.115-125, (1998).
  30. R.Zlot, A.Stentz, M.B.Dias, S.Thayer: "Multi-Robot Exploration Controlled By A Market Economy", Proceedings of the IEEE International Conference on Robotics and Automation, (2002).
  31. R.C.Arkin, T.Balch: "AuRA: Principles and Practice in Review", Journal of Experimental and Theoretical Artificial Intelligence, Vol. 9, No. 2/3, pp.175-188, (1997).
  32. D.Chevallier, S.Payandeh: "On Kinematic Geometry of Multi-Agent Manipulating System Based on the Contact Force Information", The 6 th International Conference on Intelligent Autonomous Systems (IAS-6), pp.188-195, (2000).
  33. R.Alami, S.Fleury, M.Herrb, F.Ingrand, F.Robert: "Multi-Robot Cooperation in the Martha Project", IEEE Robotics and Automation Magazine, (1997).
  34. R.C.Arkin: "Integrating Behavioral, Perceptual, and World Knowledge in Reactive Naviga- tion", Robotics and Autonomous Systems, 6:pp.105-122, (1990).
  35. T.Balch, R.Arkin: "Behavior-Based Formation Control for Multi-Robot Teams", IEEE Transactions on Robotics and Automation, December (1998).
  36. M.Benda, V.Jagannathan, R.Dodhiawalla: "On Optimal Cooperation of Knowledge Sources", Technical Report BCS-G2010-28, Boeing AI Center, August (1985).
  37. R.A.Brooks: "A Robust Layered Control System for a Mobile Robot", IEEE Journal of Robotics and Automation, RA-2(1):14-23, March (1986).
  38. T.Haynes, S.Sen: "Evolving Behavioral Strategies in Predators and Prey", In Gerard Weiss and Sandip Sen, editors, Adaptation and Learning in Multi-Agent Systems, pp.113-126. Springer, (1986).
  39. S.M.LaValle, D.Lin, L.J.Guibas, J.C.Latombe, R.Motwani: "Finding an Unpredictable Tar- get in a Workspace with Obstacles", In Proceedings of the 1997 IEEE International Confer- ence on Robotics and Automation (ICRA-97), pp.737-742, (1997).
  40. P.Svestka, M.H.Overmars: "Coordinated Path Planning for Multiple Robots", Robotics and Autonomous Systems, 23(3), pp. 125-152, (1998).
  41. V.J.Lumelsky, K.R.Harinarayan: "Decentralized Motion Planning for Multiple Mobile Robots: The Cocktail Party Model", Autonomous Robots, 4(1), pp. 121-136, (1997).
  42. C.Ferrari, E.Pagello, J.Ota, T.Arai: "Multirobot Motion Coordination in Space and Time", Robotics and Autonomous Systems, 25:219-229, (1998).
  43. A.Yamashita, M.Fukuchi, J.Ota, T.Arai, H.Asama: "Motion Planning for Cooperative Trans- portation of a Large Object by Multiple Mobile Robots in a 3D Environment", In Proceedings of IEEE International Conference on Robotics and Automation, pp. 3144-3151, (2000).
  44. D.MacKenzie, R.Arkin, J.Cameron: "Multiagent Mission Specification and Execution", Au- tonomous Robots, 4(1), pp. 29-52, (1997).
  45. M.J.Mataric: "Designing Emergent Behaviors: From Local Interactions to Collective Intel- ligence", In J.Meyer, H.Roitblat, and S.Wilson, editors, Proceedings of the Second Interna- tional Conference on Simulation of Adaptive Behavior, pp.432-441, Honolulu, Hawaii, MIT Press, (1992).
  46. J.Deneubourg, S.Goss, G.Sandini, F.Ferrari, P.Dario: "Self-Organizing Collection and Trans- port of Objects in Unpredictable Environments", In Japan-U.S.A. Symposium on Flexible Automation, pp.1093-1098, Kyoto, Japan, (1990).
  47. A.Drogoul J.Ferber: "From Tom Thumb to the Dockers: Some Experiments With Foraging Robots", In Proceedings of the Second International Conference on Simulation of Adaptive Behavior, pp.451-459, Honolulu, Hawaii, (1992).
  48. M.J.Mataric: "Interaction and Intelligent Behavior", PhD Thesis, Massachusetts Institute of Technology, (1994).
  49. P.Stone, M.Veloso: "Task Decomposition, Dynamic Role Assignment, and Low-Bandwidth Communication for Real-Time Strategic Teamwork", Artificial Intelligence, 110(2), pp. 241- 273, June, (1999).
  50. C.Candea, H.Hu, L.Iocchi, D.Nardi, M.Piaggio: "Coordinating in Multi-Agent RoboCup Teams", Robotics and Autonomous Systems, 36(2-3):67-86, August (2001).
  51. E.Pagello, A.DAngelo, C.Ferrari, R.Polesel, R.Rosati, A.Speranzon: "Emergent Behaviors of a Robot Team Performing Cooperative Tasks", Advanced Robotics, (2002).
  52. E.Pagello, A.DAngelo, F.Montesello, F.Garelli, C.Ferrari: "Cooperative Behaviors in Multi- Robot Systems Through Implicit Communication", Robotics and Autonomous Systems, 29(1), pp. 65-77, (1999).
  53. L.E.Parker: "ALLIANCE: An Architecture for Fault-Tolerant Multi-Robot Cooperation", IEEE Transactions on Robotics and Automation, 14(2), pp. 220-240, (1998).
  54. L.E.Parker, C.Touzet: "Multi-Robot Learning in a Cooperative Observation Task", In Dis- tributed Autonomous Robotic Systems 4, pp. 391-401. Springer, (2000).
  55. S.Premvuti, S.Yuta: "Consideration on the Cooperation of Multiple Autonomous Mobile Robots", In Proceedings of the IEEE International Workshop of Intelligent Robots and Sys- tems, pp. 59-63, Tsuchiura, Japan, (1990).
  56. P.K.C.Wang: "Navigation Strategies for Multiple Autonomous Mobile Robots", In Proceed- ings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 486-493, (1989).
  57. I.M. Rekleitis, G. Dudek, E. Milios: "Experiments in Free-Space Triangulation Using Co- operative Localization", IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS), (2003).
  58. J.Fredslund, M.J.Mataric: " Robot Formations Using Only Local Sensing and Control", In the proceedings of the International Symposium on Computational Intelligence in Robotics and Automation (IEEE CIRA 2001), pp. 308-313, Banff, Alberta, Canada, (2001).
  59. N.Gordon, I.A.Wagner, A.M.Bruckstein: "Discrete Bee Dance Algorithms for Pattern For- mation on a Grid", In the proceedings of IEEE International Conference on Intelligent Agent Technology (IAT03), pp. 545-549, October, (2003).
  60. T.Arai, H.Ogata, T.Suzuki: "Collision Avoidance Among Multiple Robots Using Virtual Impedance", In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 479-485, (1989).
  61. M.B.Dias, A.Stentz: "A Market Approach to Multirobot Coordination": Technical Report, CMU-RI -TR-01-26, Robotics Institute, Carnegie Mellon University, (2001).
  62. V. Yanovski, I.A. Wagner, A.M. Bruckstein: "A distributed ant algorithm for efficiently pa- trolling a network", Algorithmica, 37, pp. 165-186, (2003).
  63. I.A. Wagner, A.M. Bruckstein: "ANTS: agents, networks, trees and subgraphs", Future Gen- eration Computer Computer Systems Journal, 16(8), pp. 915-926, (2000).
  64. V. Yanovski, I.A. Wagner, A.M. Bruckstein: "Vertex-ants-walk: a robust method for efficient exploration of faulty graphs. Annals of Mathematics and Artificial Intelligence, 31(1-4), pp. 99-112, (2001).
  65. F.R. Adler, D.M. Gordon: "Information collection and spread by networks of partolling agents", The American Naturalist, 140(3), pp. 373-400, (1992).
  66. D.M. Gordon: "The expandable network of ant exploration", Animal Behaviour, 50, pp. 372- 378, (1995).
  67. Polycarpou, M., Yang, Y. and Passino, K.: "A Cooperative Search Framework for Distributed Agents", In Proceedings of the 2001 IEEE International Symposium on Intelligent Control (Mexico City, Mexico, September 5-7). IEEE, New Jersey, pp. 1-6, (2001).
  68. Koenig, S., Liu, Y.: "Terrain Coverage with Ant Robots: A Simulation Study", AGENTS'01, May 28-June 1, Montreal, Quebec, Canada, (2001).
  69. Rekleitisy, I., Lee-Shuey, V., Peng Newz, A., Choset, H.: "Limited Communication, Multi- Robot Team Based Coverage", Proceedings of the 2004 IEEE International Conference on Robotics and Automation, New Orleans, LA, April, (2004).
  70. Latimer, D., Srinivasa, S., Lee-Shue, V., Sonne, S., Choset, H., Hurst, A. :"Toward Sensor Based Coverage with Robot Teams", Proc. 2002 IEEE International Conference on Robotics and Automation, Washington DC, May, (2002).
  71. E. U. Acar, Y. Zhang, H. Choset, M. Schervish, A. G. Costa, R. Melamud, D. C. Lean, A. Gravelin : "Path Planning for Robotic Demining and Development of a Test Platform", International Conference on Field and Service Robotics, pp. 161-168, Helsinki, Finland, (2001).
  72. Z. Butler, A. Rizzi, R. Hollis : "Distributed Coverage of Rectilinear Environments", Proc. of the Workshop on the Algorithmic Foundations of Robotics, (2001).
  73. T. W. Min, H. K. Yin : "A Decentralized Approach for Cooperative Sweeping by Multiple Mobile Robots", 1998 IEEE/RSJ International Conference on Intelligent Robots and Sys- tems, vol. 1, pp. 380-385, (1998).
  74. M. A. Batalin, G. S. Sukhatme : "Spreading Out: A Local Approach to Multi-Robot Cover- age", 6th International Symposium on Distributed Autonomous Robotics Systems, Fukuoka, Japan, June, (2002).
  75. D. C. Conner, A. Greenfield, P. N. Atkar, A. A. Rizzi, H. Choset : "Paint Deposition Mod- eling for Trajectory Planning on Automotive Surfaces", IEEE Transactions on AUtomation Science and Engineering, Vol. 2, No. 4, October 2005, pp. 381-392, (2005).
  76. G. Dudek, M. R. M. Jenkin, E. Milios, D. Wilkes : "A taxonomy for multi-agent robotics", Autonomous Robots Journal, Vol. 3, No. 4, pp. 375-397, December, (1996).

Related papers

Ant–swarm robotics for a dynamic cleaning problem
Yaniv Altshuler

ROBOMAT07, 7

View PDFchevron_right
Dynamics of cooperative excavation in ant and robot collectives
Jordan Kennedy

eLife

The solution of complex problems by the collective action of simple agents in both biologically evolved and synthetically engineered systems involves cooperative action. Understanding the resulting emergent solutions requires integrating across the organismal behaviors of many individuals. Here we investigate an ecologically relevant collective task in black carpenter ants Camponotus pennsylvanicus: excavation of a soft, erodible confining corral. Individual ants show a transition from individual exploratory excavation at random locations to spatially localized collective exploitative excavation and eventual excavate out from the corral. An agent minimal continuum theory that coarse-grains over individual actions and considers their integrated influence on the environment leads to the emergence of an effective phase space of behaviors in terms of excavation strength and cooperation intensity. To test the theory over the range of both observed and predicted behaviors, we used custom-...

View PDFchevron_right
Cooperative covering by ant-robots using evaporating traces
Michael Lindenbaum

Department of Computer Science, 1996

Some insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper we investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the oor of an un-mapped building, or any task that requires the traversal of an unknown network. More speci cally, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement ...

View PDFchevron_right
Distributed covering by ant-robots using evaporating traces
Michael Lindenbaum

IEEE Transactions on Robotics and Automation, 1999

Ants and other insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper, we investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the floor of an un-mapped building, or any task that requires the traversal of an unknown region. More specifically, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement error. Our abstract model is a decentralized multiagent adaptive system with a shared memory, moving on a graph whose vertices are the floor-tiles. We describe three methods of covering a graph in a distributed fashion, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. As opposed to existing traversal methods (e.g., depth first search), our algorithms are adaptive: they will complete the traversal of the graph even if some of the a(ge)nts die or the graph changes (edges/vertices added or deleted) during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time.

View PDFchevron_right
Swarm Robotics for a Dynamic Cleaning Problem
Yaniv Altshuler

Proceedings 2005 IEEE Swarm Intelligence …, 2005

Several recent works considered multi agents robotics in static environments (e.g. \cite{CC}, \cite{GraphSearch1}, \cite{CoopRobot1} and others). In this work we examine ways of operating in dynamic environments, in which changes may take place regardless of the agents' activity. The work focuses on a dynamic variant of the known \emph{Cooperative Cleaners} problem (described and analyzed in~\cite{CC}). This problem assumes a grid, part of which is ``dirty'', when the ``dirty'' part is a connected region of the grid. On this dirty region several agents move, each having the ability to ``clean'' the place it is located in. The dynamic variant of the problem involves a deterministic evolution of the environment, simulating a spreading \emph{contamination}, or \emph{fire}. A cleaning protocol for the problem is presented, as well as several analytic bounds for it. In addition, the work contains simulative results for the proposed protocol.

View PDFchevron_right

Related topics

  • Collective Intelligence
  • Swarm Intelligence
  • Swarm Robotics
  • Swarm Algorithm
  • Cooperative Search
  • Swarm Search
  • Cooperative Cleaners
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved