Advertisement

Autonomous Robots

, Volume 11, Issue 2, pp 149–171 | Cite as

Collaboration Through the Exploitation of Local Interactions in Autonomous Collective Robotics: The Stick Pulling Experiment

  • Auke Jan Ijspeert
  • Alcherio Martinoli
  • Aude Billard
  • Luca Maria Gambardella
Article

Abstract

This article presents an experiment which investigates how collaboration in a group of simple reactive robots can be obtained through the exploitation of local interactions. A test-bed experiment is proposed in which the task of the robots is to pull sticks out of the ground—an action which requires the collaboration of two robots to be successful. The experiment is implemented in a physical setup composed of groups of 2 to 6 Khepera robots, and in Webots, a 3D simulator of Khepera robots.

The results using these two implementations are compared with the predictions of a probabilistic modeling methodology (A. Martinoli, A. Ijspeert, and F. Mondada, 1999, Robotics and Autonomous Systems, 29:51–63, 1999; A. Martinoli, A. Ijspeert, and L. Gambardella, 1999, in Proceedings of Fifth European Conference on Artificial Life, ECAL99, Lecture Notes in Computer Science, Springer Verlag: Berlin, pp. 575–584) which is here extended for the characterization and the prediction of a collaborative manipulation experiment. Instead of computing trajectories and sensory information, the probabilistic model represents the collaboration dynamics as a set of stochastic events based on simple geometrical considerations. It is shown that the probabilistic model qualitatively and quantitatively predicts the collaboration dynamics. It is significantly faster than a traditional sensor-based simulator such as Webots, and its minimal set of parameters allows the experimenter to better identify the effect of characteristics of individual robots on the team performance.

Using these three implementations (the real robots, Webots and the probabilistic model), we make a quantitative investigation of the influence of the number of workers (i.e., robots) and of the primary parameter of the robots' controller—the gripping time parameter—on the collaboration rate, i.e., the number of sticks successfully taken out of the ground over time. It is found that the experiment presents two significantly different dynamics depending on the ratio between the amount of work (the number of sticks) and the number of robots, and that there is a super-linear increase of the collaboration rate with the number of robots. Furthermore, we investigate the usefulness of heterogeneity in the controllers' parameters and of a simple signalling scheme among the robots. Results show that, compared to homogeneous groups of robots without communication, heterogeneity and signalling can significantly increase the collaboration rate when there are fewer robots than sticks, while presenting a less noticeable or even negative effect otherwise.

collective autonomous robotics swarm intelligence collaboration sensor-based simulation probabilistic modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Balch, T. and Arkin, R. 1994. Communication in reactive multiagent robotic systems. Autonomous Robots, 1(1):27-52.Google Scholar
  2. Balch, T. and Arkin, R. 1998. Behavior-based formation control for multirobots teams. IEEE Trans. on Robotics and Automation, 14(6):926-939.Google Scholar
  3. Beckers, R., Holland, O., and Deneubourg, J.-L. 1994. From local actions to global tasks: Stigmergy and collective robotics. In Proc. of the Fourth Workshop on Artificial Life, R. Brooks and P. Maes (Eds.), MIT Press: Cambridge, MA, pp. 181-189.Google Scholar
  4. Beni, G. and Wang, J. 1989. Swarm intelligence. In Proc. of the Seventh Annual Meeting of the Robotics Society of Japan, Tokyo, Japan, pp. 425-428.Google Scholar
  5. Billard, A., Ijspeert, A., and Martinoli, A. 1999. A multi-robot system for adaptive exploration of a fast changing environment: Probabilistic modelling and experimental study. Connection Science, 11(3/4):359-379.Google Scholar
  6. Boehringer, K., Brown, R., Donald, B., Jennings, J., and Rus, D. 1995. Distributed robotic manipulation: Experiments in minimalism. In Proc. of the Fourth Int. Symp. on Experimental Robotics, Stanford, O. Khatib and J.K. Salisbury (Eds.), Lecture Notes in Control and Information Sciences, Springer Verlag: Berlin, pp. 11-25.Google Scholar
  7. Bonabeau, E., Dorigo, M., and Theraulaz, G. 1999. Swarm Intelligence: From Natural to Artificial Systems, SFI Studies in the Science of Complexity, Oxford University Press: Oxford, UK.Google Scholar
  8. Chauvin, R. and Janin, P. 1975. Facteurs de direction et d'excitation au cours de l'accomplissement d'une tache chez formica polyctena. Insectes Sociaux, 22:199-206.Google Scholar
  9. Everett, H.R., Gilbreath, G.A., Heath-Pastore, T.A., and Laird, R.T. 1993. Coordinated control of multiple security robots. In Mobile Robots VII, SPIE: Arlington, VA, vol. 2058, pp. 292-305.Google Scholar
  10. Fujita, T. and Kimura, H. 1998. Tight cooperative working system by multiple robots. In Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE Computer Society: Los Alamitos, CA, pp. 1405-1410.Google Scholar
  11. Gage, D. 1995. Many-robots MCM search systems. In Proc. of the Autonomous Vehicles in Mine Countermeasure Symposium, Monterey, CA, A. Bottoms, J. Eagle, and H. Bayless (Eds.), pp. 955-933.Google Scholar
  12. Ghanea-Hercock, R. and Barnes, D.P. 1996. An evolved fuzzy reactive control system for co-operating autonomous robots. In Proc. of the Fourth Int. Conf. on Simulation of Adaptive Behavior: From Animals to Animats, Cape Cod, MA, P. Maes, M.J. Mataric, J.-A. Meyer, J. Pollack, and S.W.Wilson (Eds.), MIT Press: Cambridge, MA, pp. 599-607.Google Scholar
  13. Hayes, A.T., Martinoli, A., and Goodman, R.M. 2000. Comparing distributed exploration strategies with simulated and real autonomous robots. In Proc. of the Fifth Int. Symp. on Distributed Autonomous Robotic Systems DARS-00, Knoxville, TN, L. Parker, G. Bekey, and J. Bahren (Eds.), Springer Verlag: Berlin, pp. 261-270.Google Scholar
  14. Holland, O. and Melhuish, C. 1999. Stigmergy, self-organization, and sorting in collective robotics. Artificial Life, 5:173-202.Google Scholar
  15. Hosokawa, K., Tsujimori, T., Fujii, T., Kaetsu, H., Asama, H., Kuroda, Y., and Endo, I. 1998. Mechanisms for self-organizing robots which reconfigure in a vertical plane. In Proc. of the Fourth Int. Symp. on Distributed Autonomous Robotic Systems, Karlsruhe, Germany, T. Lueth, R. Dillman, P. Dario, and H.Wuern (Eds.), Springer Verlag: Berlin, pp. 111-118.Google Scholar
  16. Humberstone, C.K. and Smith, K.B. 2000. Object transport using multiple mobile robots wth pin joint endeffectors. In Proc. of the Fifth Int. Symp. on Distributed Autonomous Robotic Systems, DARS-00, Knoxville, TN, L.E. Parker, G. Bekey, and J. Barhen (Eds.), Springer Verlag: Berlin, pp. 417-426.Google Scholar
  17. Johnson, P. and Bay, J. 1995. Distributed control of simulated autonomous mobile robot collectives in payload transportation. Autonomous Robots, 2:43-63.Google Scholar
  18. Khatib, O. 1999. Mobile manipulation: The robotic assistant. Robotics and Autonomous Systems, 26:175-183.Google Scholar
  19. Krieger, M.B. and Billeter, J.-B. 2000. The call of duty: Selforganised task allocation in a population of up to twelve mobile robots. Robotics and Autonomous Systems, 30(12):65-84.Google Scholar
  20. Kube, C.R. and Bonabeau, E. 2000. Cooperative transport by ants and robots. Robotics and Autonomous Systems, 30(12):85-101.Google Scholar
  21. Martinoli, A. 1999. Swarm intelligence in autonomous collective robotics: From tools to the analysis and synthesis of distributed control strategies. Unpublished doctoral dissertation, Ph.D. Thesis No 2069, EPFL.Google Scholar
  22. Martinoli, A., Franzi, E., and Matthey, O. 1997. Towards a reliable set-up for bio-inspired collective experiments with real robots. In Proc. of the Fifth Int. Symp. on Experimental Robotics, Barcelona Spain, A. Casals and A. de Almeida (Eds.), Lecture Notes in Control and Information Sciences, Springer Verlag: Berlin, pp. 597-608.Google Scholar
  23. Martinoli, A., Ijspeert, A., and Mondada, F. 1999a. Understanding collective aggregation mechanisms: From probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1):51-63.Google Scholar
  24. Martinoli, A., Ijspeert, A., and Gambardella, L. 1999b. A probabilistic model for understanding and comparing collective aggregation mechanisms. In Proceedings of the Fifth European Conference on Artificial Life, ECAL99, D. Floreano, F. Mondada, and J.-D. Nicoud (Eds.), Lecture Notes in Computer Science, Springer Verlag: Berlin, pp. 575-584.Google Scholar
  25. Martinoli, A. and Mondada, F. 1995. Collective and cooperative group behaviours: Biologically inspired experiments in robotics. In Proc. of the Fourth Int. Symp. on Experimental Robotics, O. Khatib and J.K. Salisbury (Eds.), Lecture Notes in Control and Information Sciences, Springer Verlag: Berlin, pp. 3-10.Google Scholar
  26. Mataric, M. 1994. Interaction and intelligent behavior. Unpublished doctoral dissertation, Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA.Google Scholar
  27. Mataric, M., Nilsson, M., and Simsarian, K. 1995. Cooperative multirobot box-pushing. In Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE Computer Society, pp. 556-561.Google Scholar
  28. Michel, O. 1998.Webots: Symbiosis between virtual and real mobile robots. In Proc. of the First Int. Conf. on Virtual Worlds, Paris, France, J.-C. Heudin (Ed.), Springer Verlag: Berlin, pp. 254-263. (See also http://www.cyberbotics.com/webots/).Google Scholar
  29. Mondada, F., Franzi, E., and Ienne, P. 1993. Mobile robot miniaturization: A tool for investigation in control algorithms. In Proc. of the Third Int. Symp. on Experimental Robotics, T. Yoshikawa and F. Miyazaki (Eds.), Lecture Notes in Control and Information Sciences, Springer Verlag: Berlin, pp. 501-503.Google Scholar
  30. Ota, J. and Arai, T. 1999. Transfer control of a large object by a group of mobile robots. Robotics and Autonomous Systems, 28:271-280.Google Scholar
  31. Parker, L. 1994. ALLIANCE: An architecture for fault tolerant, cooperative control of heterogeneous mobile robots. In Proc. 1994 IEEE/RSJ/GI Int. Conf. Intell. Robot. Systems (IROS'94), Munich, Germany, pp. 776-783.Google Scholar
  32. Parker, L. 1998. ALLIANCE: An architecture for fault tolerant multirobot cooperation. IEEE Trans. on Robotics and Automation, 14(2):220-240.Google Scholar
  33. Parker, L. 2000. Lifelong adaptation in heterogeneous multi-robots teams: Reponse to continual variation. Autonomous Robots, 8(3):239-267.Google Scholar
  34. Touzet, C. 2000. Robot awareness in cooperative mobile robot learning. Autonomous Robots, 8(1):87-97.Google Scholar
  35. Wang, J. and Premvuti, S. 1995. Distributed traffic regulation and control for multiple autonomous mobile robots operating in discrete space. In Proc. of the IEEE Int. Conf. on Robotics and Automation, IEEE Computer Society: Los Alamitos, CA, pp. 1619-1624.Google Scholar
  36. Wang, Z.D., Kimura, Y., Takahashi, T., and Nakano, E. 2000. A control method of a multiple non-holonomic robot system for cooperative transportation. In Proc. of the Fifth Int. Symp. on Distributed Autonomous Robotic Systems, DARS-00, Knoxville, TN, L.E. Parker, G. Bekey, and J. Barhen (Eds.), Springer Verlag: Berlin, pp. 447-456.Google Scholar
  37. Yamauchi, B. 1999. Decentralized coordination for multirobot exploration. Robotics and Autonomous Systems, 29(1):111-118.Google Scholar
  38. Yoshida, E., Arai, T., Yamamoto, M., and Ota, J. 1998. Local communication of multiple mobile robots: Design of optimal communication area for cooperative tasks. J. of Robotic Systems, 15(7):407-419.Google Scholar
  39. Yoshida, E., Murata, S., Tomita, K., Kurokawa, H., and Kokaji, S. 1999. An experimental study on a self-repairing modular machine. Robotics and Autonomous Systems, 29(1):79-89.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Auke Jan Ijspeert
    • 1
  • Alcherio Martinoli
    • 2
  • Aude Billard
    • 1
  • Luca Maria Gambardella
    • 3
  1. 1.Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Lugano, Switzerland; Laboratoire de Microinformatique (LAMI), EPFLLausanneSwitzerland
  2. 2.Laboratoire de Microinformatique (LAMI), EPFLLausanneSwitzerland
  3. 3.Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)LuganoSwitzerland

Personalised recommendations