  This paper considers a multiagent, connected, robotic fleet where the primary
functionality of the agents is sensing. A distributed multi-sensor control
strategy maximizes the value of the collective sensing capability of the fleet,
using an information-driven approach. Each agent individually performs sensor
processing (Kalman Filtering and Joint Probabilistic Data Association) to
identify trajectories (and associated distributions). Using communications with
its neighbors the agents enhance the prediction of the trajectories using a
Consensus of Information approach that iteratively calculates the
Kullback-Leibler average of trajectory distributions, enabling the calculation
of the value of the collective information for the fleet. The dynamics of the
agents, the evolution of the identified trajectories for each agent, and the
dynamics of individual observed objects are captured as a Partially Observable
Markov Decision Process (POMDP). Using this POMDP and applying rollout with
receding horizon control, an optimized non-myopic control policy that maximizes
the collective fleet information value is synthesized. Simulations are
performed for a scenario with three heterogeneous UAVs performing coordinated
target tracking that illustrate the proposed methodology and compare the
centralized approach with a contemporary sequential multiagent distributed
decision technique.
