Cooperative real-time search and task allocation in UAV teams

TLDR: This paper considers a heterogeneous team of UAVs drawn from several distinct classes and engaged in a search and destroy mission over an extended battlefield, and presents a simple cooperative approach based on distributed assignment mediated through centralized mission status information.

Abstract: In this paper, we consider a heterogeneous team of UAVs drawn from several distinct classes and engaged in a search and destroy mission over an extended battlefield. Several different types of targets are considered. Some target locations are suspected a priori with a certain probability, while the others are initially unknown. During the mission, the UAVs perform Search, Confirm, Attack and Battle Damage Assessment (BDA) tasks at various locations. The target locations are detected gradually through search, while the tasks are determined in real-time by the actions of all UAVs and their results (e.g., sensor readings), which makes the task dynamics stochastic. The tasks must, therefore, be allocated to UAVs in real-time as they arise. Each class of UAVs has its own sensing and attack capabilities with respect to the different target types, so the need for appropriate and efficient assignment is paramount. We present a simple cooperative approach to this problem, based on distributed assignment mediated through centralized mission status information. Using this information, each UAV assesses the task opportunities available to it, and makes commitments through a phased incremental process. This produces a simple, flexible, scalable and inherently decentralizable method for task allocation. Concurrently, every UAV also monitors the degree to which various parts of the environment have been searched, and accommodates this information in planning its paths. We study the effect of various decision parameters, target distributions, and UAV team characteristics on the performance of our approach.