The efficacy of the developed multi-vehicle cooperative control framework has been demonstrated in a cooperative road-search mission scenario involving multiple fixed-wing UAVs. The mission is initiated by a minimally trained user who specifies a road of interest on a digital map. The coordinates of this road are then transmitted over the network to a fleet of small tactical UAVs equipped with complementary visual sensors. Decentralized optimization algorithms autonomously generate feasible flight trajectories that maximize road coverage and account for sensor capabilities (field of view, resolution, and gimbal constraints) as well as inter-vehicle and ground-to-air communications limitations. The fleet of UAVs then starts the cooperative road search. During this phase, the information obtained from the sensors mounted onboard the UAVs is shared over the network and retrieved by remote users in near real time. Target detection can thus be done remotely on the ground, based on in-situ imagery data delivered over the network.

 

In this particular mission scenario, our cooperative control strategies improve mission performance and provide reliable target discrimination, by effectively combining the capabilities of the onboard sensors. In fact, flying in a coordinated fashion is what allows, for example, to maximize the overlap of the fields of view of multiple sensors and to take full advantage of complementary sensors.

 

Details about this project, including the flight test experiments conducted by NPS, can be found in:

1. E. Xargay, V. Dobrokhodov, I. Kaminer, A. Pascoal, N. Hovakimyan, and C. Cao. Time-critical cooperative control for multiple autonomous vehicles. In IEEE Control Systems Magazine, Vol: 32, Issue: 5, 2012.
2. E. Xargay, I. Kaminer, A. Pascoal, N. Hovakimyan, V. Dobrokhodov, V. Cichella, P. Aguiar, and R. Ghabcheloo. Time-critical cooperative path following of multiple UAVs over time-varying networks. In Journal of Guidance, Control, and Dynamics, Vol. 36, No. 2, 2013.