This paper presents results on the guidance and control
of fleets of cooperating Unmanned Aerial Vehicles (UAVs). A key challenge for
these systems is to develop an overall control system architecture that can
perform optimal coordination of the fleet, evaluate the overall fleet performance
in real-time, and quickly reconfigure to account for changes in the environment
or the fleet. The optimal fleet coordination problem includes team composition
and goal assignment, resource allocation, and trajectory optimization. These
are complicated optimization problems for scenarios with many vehicles, obstacles,
and targets. Furthermore, these problems are strongly coupled, and optimal coordination
plans cannot be achieved if this coupling is ignored. This paper presents an
approach to the combined resource allocation and trajectory optimization aspects
of the fleet coordination problem which calculates and communicates the key
information that couples the two. Also, this approach permits some steps to
be distributed between parallel processing platforms for faster solution. This
algorithm estimates the cost of various trajectory options using the distributed
platforms and then solves a centralized assignment problem to minimize the mission
completion time. The detailed trajectory planning for this assignment can then
be distributed back to the platforms. During execution, the coordination and
control system reacts to changes in the fleet or the environment. The overall
approach is demonstrated on several example scenarios to show multi-task allocation
and cooperative path planning prior to the mission and to show dynamic re-planning
to account for changes in the environment during execution.
Download: Paper
Professor Jonathan
P. How
jhow@mit.edu
November 24, 2002