Integrated Control, Coordination, and Networked Communication in Multivehicle Systems
National Science Foundation CCF-0728983; collaboration with T. Javidi and A. Scaglioni
(National Science Foundation CAREER Award, Air Force Office of Scientific Research FA9550-05-1-0430, STTR FA9550-05-C-0112 and The Boeing Company; collaboration with T. Javidi, J. Vagners, R. Rysdyk, and Insitu)
Unlike most coordinated control settings, we are not only interested in the question of formation control, but also the question of reliably providing the communication necessary to achieve the coordination. Each vehicle is equipped with physical devices that provide local information from sensing and global information from communication. Sensing devices are assumed to be lower cost in terms of power, computation, and range of operation, while communication devices are comparatively higher cost but with more information density and reconfigurability. While each vehicle is equipped with a communication device, not all vehicles will be allowed to use these devices due to resource limitations, environmental constraints such as bandwidth, and/or failure of the device. Such scenarios are typical of multiple autonomous aircraft in urban settings or multiple autonomous underwater vehicles in shipyards.
The approach taken for controller design is a superposition of spacing control and heading control. The heading control is derived from Kuramoto models of oscillators. When state data is transferred via dynamic communication, a natural discretization occurs. From the control perspective, a study of the resulting discretized dynamics can relate the discretization parameter Δ, delay in data transmission, and stabilizing values of the oscillator coupling gain K. Further, truncated data holds during update intervals can be shown to be more effective than typical zero order hold approaches. From the communication perspective, the discretization parameter Δ can be used to relate optimal network topologies to the broadcast of B bits of data. These optimization results indicate that single hop networks can outperform multi-hop schemes when the amount of data relative to the discretization interval is above a threshold.
The performance of our proposed communication and control algorithms will be evaluated not only in simulation, but also in an existing 3D autonomous vehicle testbed at the University of Washington. The facility consists of three autonomous underwater vehicles linked by wireless communication and an instrumented water tank facility. The wireless communication is currently implemented via radio frequency and a vision-based tracking system. In implementation this communication method can be constrained to emulate characteristics of a variety of environments and hardware.
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