This paper presents a novel approach for the coordination of a team of autonomous sensor
platforms searching for lost targets under uncertainty. A real-time receding horizon controller
in continuous action space is developed based on a decentralized gradient-based
optimization algorithm and by using the expected observation as an estimate of future
rewards. The expected observation is a cost-to-go heuristic that estimates the goodness of
the states that the platforms could reach. It permits the decision making algorithm to take into …

This paper presents a novel contribution to the problem of coordinating a team of
autonomous sensor agents searching for targets in a large scale environment. Team
negotiation is performed using a decentralized gradient-based optimization algorithm.
Conventional approaches use finite differencing to approximate the gradient information that
is computationally less efficient and exposes the gradient-based optimizer to potential
numerical errors and instability. The novelty of our work is the explicit formulation of the …