Distributed Cooperative Search with Collision Avoidance for a Team of Unmanned Aerial Vehicles Using Gradient Optimization

This paper addresses the cooperative search problem for a team of unmanned aerial vehicles (UAVs) with limited field-of-view (FOV) and available overload constraints. First, the models of environment, UAV, image sensor, and communication are established. Second, a modified distributed information fusion strategy based on the Bayesian rule is proposed, which can make all the individual probability maps converge to the same one and reflect the true existence or nonexistence of targets within each cell. Third, a distributed gradient-based optimization method for path planning involved in the cooperative search is proposed and the available overload constraint of the UAV can be taken into account. Fourth, the aforementioned distributed gradient-based optimization method is improved by involving collision avoidance, establishing reasonable safety distance constraints, and using the Lagrange multiplier. Finally, the correctness and effectiveness of the proposed method are validated and demonstrated via simulations. (C) 2016 American Society of Civil Engineers.