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...tion, that is, each agent can compute its own cell independently from its teammates provided that is aware of the positions and velocities of its neighboring agents. Numerical simulations that illustrate the theoretical developments are also presented. Key words: Autonomous agents, Voronoi diagrams, partitioning algorithms, finite-time control, computational methods. 1 Introduction The importance of using state-dependent proximitymetrics in partitioning problems related to applications of multi-agent dynamical systems was first identified in [3] and subsequently extended in a series of papers [1, 2, 4, 13]. In particular, as is stressed in [3], the use of statedependent proximity metrics leads to spatial partitions that encode proximity information that succinctly captures the dynamic characteristics of the agents; something that cannot be achieved with more standard spatial partitions [11], whose proximity metrics stem from geometric considerations primarily. A typical problem in which the proposed class of spatial partitions could play a key role is the following: A team of agents are seeking for an optimal way to subdivide a common subset of their state space such that they can share among t...

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...sired destination. An increasing body of work in the systems and control literature deals with cooperative networks of agents estimating spatial natural phenomena, including ocean [49, 68, 33], river =-=[78]-=-, and hurricane sampling [21]. In the problem considered here, drogues are able to actuate their depth through buoyancy changes, but are completely subject to the force of the internal wave in the flo...

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...9) by the Association for the Sciences of Limnology and Oceanography, Inc., see [1] for additional information. of agents estimating spatial natural phenomena, including ocean [17], [18], [19], river =-=[20]-=-, and hurricane sampling [21]. This work builds off our previous work [22], which considers a similar estimation problem for linear internal waves. The nonlinear wave case considered here presents nov...