Distributed Mission Control for Unmanned Air Vehicles in Stochastic Environments
Abstract
This research focused on developing of theoretical and algorithmic foundations for an applicable theory of cooperative mission control for teams of heterogeneous UAVs, motivated by the problem of coordination of activities in adaptive response to unknown events. The main results of the work include: (1) new techniques for the solution of adaptive search and sensor management, solving large scale combinatorial stochastic, dynamic optimization problems, based on integration of stochastic control and discrete optimization techniques, (2) distributed control techniques for trajectories of agents performing search and tracking while having to maintain communications connectivity; (3) cooperative control techniques for mission management involving rendezvouz problems of multiple agents performing tasks; (4) distributed algorithms for nonlinear resource allocation problems to agents; and (5) combinatorial algorithms for managing connectivity of air-to-air low directional communication networks. These results provide new models and algorithms for cooperative control that increase the level of autonomy that can be provided to UAVs, thereby enhancing the U. S. Air Force's capability to use unmanned vehicles without requiring large numbers of human operators.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 10, 2010
- Accession Number
- ADA567152
Entities
People
- Christos G. Cassandras
- David A. Castañón
Organizations
- Boston University