Networked Autonomous Vehicles Research Articles

Stable task load balancing strategies for Cooperative control of networked autonomous air vehicles

We introduce a mathematical model for the study of cooperative control problems for multiple autonomous air vehicles (AAVs) connected via a communication network. We propose a cooperative control strategy based on task-load balancing that seeks to ensure that no vehicle is underutilized and we show how to characterize task-load balancing as a stability property. Then, using Lyapunov stability analysis, we provide conditions under which task-load balancing is achieved even in the presence of communication delays. Finally, we investigate performance properties of the cooperative controller using Monte Carlo simulations. This shows the benefits of cooperation and the effects of network delays and communication topology on performance

Autonomous underwater vehicle networks as integrated acoustic observation systems

Autonomous Underwater Vehicles (AUV) are rapidly being transitioned into operational systems for national defense, offshore exploration, and ocean science. However, the use the robotic platforms as components of integrated acoustic sensing systems is still at an early stage. Thus, for example, with recent advances in underwater navigation and communication, and collaborative robotics, networks of AUVs may have significant potential as moving tomographic networks for optimally and adaptively providing in-situ environmental estimates. Such concepts are important to the development and operation of environmenatally adaptive, autonomous littoral surveillance systems by providing environmental inversion specifically emphasizing the parameters significant for the actual platform configuration. Also such multi-AUV concepts have significant potential for subbottom characterization in the deep ocean, of increased importance in the offshore industry. For such systems to be operationally feasible, the limited underwater communication bandwidth makes it necessary for such systems to involve significant on-board processing, in turn requiring the acoustic sensing to be fully integrated with an onboard modeling capability and the platform navigation and control. The development of such new nested, distributed processing concepts, autonomously integrating sensing, modeling and control, is a significant challenge to the acoustics community. [Work supported by ONR.]