About: Ad-hoc Heterogeneous (MAV-UGV) Formations Stabilized Under a Top-View Relative Localization     Goto   Sponge   NotDistinct   Permalink

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  • A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions.
  • A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions. (en)
Title
  • Ad-hoc Heterogeneous (MAV-UGV) Formations Stabilized Under a Top-View Relative Localization
  • Ad-hoc Heterogeneous (MAV-UGV) Formations Stabilized Under a Top-View Relative Localization (en)
skos:prefLabel
  • Ad-hoc Heterogeneous (MAV-UGV) Formations Stabilized Under a Top-View Relative Localization
  • Ad-hoc Heterogeneous (MAV-UGV) Formations Stabilized Under a Top-View Relative Localization (en)
skos:notation
  • RIV/68407700:21230/13:00210720!RIV14-MSM-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GPP103/12/P756), P(LH11053)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 59557
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/13:00210720
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Model Predictive Control; Autonomous Mobile Robots; Compact Formations; Trajectory Planning; Aerial Vehicles (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [1855B4703B3A]
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Přeučil, Libor
  • Saska, Martin
  • Vonásek, Vojtěch
  • Báča, Tomáš
http://localhost/t...ganizacniJednotka
  • 21230
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