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Control of Mobile Robot Formations Using Aerial Cameras

  • Miguel Aranda
  • Gonzalo López-Nicolás
  • Carlos Sagüés
Chapter
Part of the Advances in Industrial Control book series (AIC)

Abstract

Cameras are versatile and relatively low-cost sensors that provide a lot of useful data. Thanks to these remarkable properties, it is possible to envision a range of different setups when considering vision-based multirobot control tasks. For instance, the vision sensors may be carried by the robots that are to be controlled, or external to them. In addition, cameras can be used in the context of both centralized and distributed control strategies. In this chapter, a system setup relying on external cameras and the two-view homography is proposed, to achieve the objective of driving a set of robots moving on the ground plane to a desired geometric formation. In particular, we propose to use multiple unmanned aerial vehicles (UAVs) as control units. Each of them carries a camera that observes a subset of the ground robotic team and is employed to control it. This gives rise to a partially distributed multirobot control method, which aims to combine the optimality and simplicity of centralized approaches with the scalability and robustness of distributed strategies. Relying on a homography computed for each of the UAV-mounted cameras, our method is purely image-based and has low computational cost. We formally study its stability for unicycle-type robots. In order for the multirobot system to converge to the target formation, certain intersections must be maintained between the sets of ground robots seen by the different cameras. To this end, we also propose a distributed strategy to coordinately control the motion of the cameras by using communication of their gathered information. The effectiveness of the proposed vision-based controller is illustrated via simulations and experiments with real robots .

Keywords

Mobile Robot Control Unit Unman Aerial Vehicle Ground Plane Camera Motion 
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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Miguel Aranda
    • 1
  • Gonzalo López-Nicolás
    • 2
  • Carlos Sagüés
    • 2
  1. 1.ISPRSIGMA Clermont, Institut PascalAubièreFrance
  2. 2.Instituto de Investigación en Ingeniería de AragónUniversidad de ZaragozaZaragozaSpain

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