Generating Maneuvers for Groups of Networked UAVs

Mihail Pivtoraiko, Rattanachai Ramaithitima, and Vijay Kumar. Generating Maneuvers for Groups of Networked UAVs. In Proceedings of the Workshop on Networked Multi-Agent Systems at the IEEE International Conference on Robotics and Automation, 2013.

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Abstract

We study the problem of real-time generation of complex maneuvers for groups of micro-UAVs operating in cluttered, partially known environments. There are two key steps to our methodology. First, the flight dynamics of the micro-UAVs and their aerodynamic interactions are represented by a pre-computed set of motion primitives. Second, an on-line planning algorithm computes feasible motions for the group without the expensive computations required for simulating the entire system and without explicitly representing the joint space. We describe the automated generation of motion primitives and discuss their use in incremental motion planning in partially-known environments. We use simulations and experimental results to demonstrate the application of the approach in representative scenarios, including the exploration of unknown spaces with limited communication.

BibTeX

@INCOLLECTION{pivtoraiko_ramaithitima_kumar_icra13,
  author = {Mihail Pivtoraiko and Rattanachai Ramaithitima and Vijay Kumar},
  title = {Generating Maneuvers for Groups of Networked UAVs},
  booktitle = {Proceedings of the Workshop on Networked Multi-Agent Systems at the IEEE International Conference on Robotics and Automation},
  year = {2013},
  abstract = {We study the problem of real-time generation of complex
                  maneuvers for groups of micro-UAVs operating in
                  cluttered, partially known environments. There are
                  two key steps to our methodology. First, the flight
                  dynamics of the micro-UAVs and their aerodynamic
                  interactions are represented by a pre-computed set
                  of motion primitives. Second, an on-line planning
                  algorithm computes feasible motions for the group
                  without the expensive computations required for
                  simulating the entire system and without explicitly
                  representing the joint space. We describe the
                  automated generation of motion primitives and
                  discuss their use in incremental motion planning in
                  partially-known environments.  We use simulations
                  and experimental results to demonstrate the
                  application of the approach in representative
                  scenarios, including the exploration of unknown
                  spaces with limited communication.}, 
bib2html_pubtype = {Workshop Papers}, 
bib2html_rescat = {Kinodynamic Planning},  
}

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