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Multi-UAV target search using explicit decentralized gradient-based negotiation

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 gradient for the target search problem that significantly enhances the efficiency in gradient evaluation and robustness for the gradient-based optimization algorithm. We present results by firstly showing the computational advantage and robustness of this explicit gradient model against the finite differencing approach and further demonstrate its application in simulation by coordinating multiple UAVs searching a large scale environment in a decentralized network.