Modeling and Optimization of a Swarm-Intelligent Inspection System

  • Nikolaus Correll
  • Alcherio Martinoli


We present a simple, behavior-based, distributed control algorithm to inspect a regular structure with a swarm of autonomous, miniature robots, using only on-board, local sensors. To estimate intrinsic advantages and limitations of the proposed control solution, we capture its characteristics at a higher abstraction level using non-spatial probabilistic microscopic and macroscopic models. Both models achieve consistent prediction on the chosen swarm metric and deliver a series of interesting qualitative and quantitative insights on further, counterintuitive, improvement of the distributed control algorithm. Modeling results were validated by experiments with one to twenty robots using a realistic simulator in the framework of a case study concerned with the inspection of a jet turbine.


Finite State Machine Microscopic Model Real Robot Team Size Swarm Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    W. Agassounon, A. Martinoli, and K. Easton. Macroscopic modeling of aggregation experiments using embodied agents in teams of constant and time-varying sizes. Autonomous Robots, 17(2–3): 163–191, 2004.CrossRefGoogle Scholar
  2. 2.
    R. Arkin. Behavior-Based Robotics. The MIT press, Cambridge, Massachusetts, 2000.Google Scholar
  3. 3.
    N. Correll and A. Martinoli. Collective inspection of regular structures using a swarm of miniature robots. In Proceedings of the 9th International Symposium of Experimental Robotics (ISER). Singapure. Springer Tracts for Autonomous Robotics (STAR). To Appear, 2004.Google Scholar
  4. 4.
    K. Lerman and A. Galstyan. Mathematical model of foraging in a group of robots: Effect of interference. Autonomous Robots, 2(13):127–141, 2002.CrossRefGoogle Scholar
  5. 5.
    K. Martin and C.V. Stewart. Real time tracking of borescope tip pose. Image and Vision Computing, 10(18):795–804, July 2000.CrossRefGoogle Scholar
  6. 6.
    A. Martinoli, K. Easton, and W. Agassounon. Modeling swarm robotic systems: A case study in collaborative distributed manipulation. Int. Journal of Robotics Research, 23(4):415–436, 2004.CrossRefGoogle Scholar
  7. 7.
    O. Michel. Webots: Professional mobile robot simulation. Journal of Advanced Robotic Systems, 1(1):39–42, 2004.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Nikolaus Correll
    • 1
  • Alcherio Martinoli
    • 1
  1. 1.Swarm-Intelligent Systems GroupNonlinear Systems LaboratoryLausanneSwitzerland

Personalised recommendations