On the Decentralized Cooperative Control of Multiple Autonomous Vehicles

  • Michael J. Hirsch
  • Daniel Schroeder
Reference work entry


This chapter is concerned with dynamically determining appropriate flight patterns for a set of autonomous UAVs in an urban environment, with multiple mission goals. The UAVs are tasked with searching the urban region for targets of interest and tracking those targets that have been detected. It is assumed that there are limited communication capabilities between the UAVs and that there exist possible line of sight constraints between the UAVs and the targets. Each UAV (i) operates its own dynamic feedback loop, in a receding-horizon framework, incorporating local information (from UAV i perspective) as well as remote information (from the perspective of the “neighbor” UAVs) to determine the task to perform and the optimal flight path of UAV i over the planning horizon. This results in a decentralized and more realistic model of the real- world situation. As the coupled task assignment and flight route optimization formulation is NP-hard, a hybrid heuristic for continuous global optimization is developed to solve for the flight plan and tasking over the planning horizon. Experiments are considered as communication range between UAVs varies.


Planning Horizon Flight Path Greedy Randomize Adaptive Search Procedure Vehicle Rout Problem Autonomous Vehicle 
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 Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Intelligence, Information, and ServicesRaytheon CompanyMaitlandUSA
  2. 2.Intelligence, Information, and ServicesRaytheon CompanyState CollegeUSA

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