Abstract-In the future, it may be possible to employ large numbers of autonomous marine vehicles to perform tedious and dangerous tasks, such as minesweeping. Hypothetically, groups of vehicles may leverage their numbers by cooperating. A fundamental form of cooperation is to perform tasks while maintaining a geometric formation. The formation behavior can then enable other cooperative behaviors. In this paper, we describe a leader-follower formation-flying control algorithm. This algorithm can be applied to one-, two-, and threedimensional formations, and contains a degree of built-in robustness. Simulations and experiments are described that characterize the performance of the formation control algorithm. The experiments utilized surface craft that were equipped with an acoustic navigation and communication system, representative of the technologies that constrain the operation of underwater autonomous vehicles. The simulations likewise included the discrete-time nature of the communication and navigation.

Abstract-In the future, it may be possible to employ large numbers of autonomous marine vehicles to perform tedious and dangerous tasks, such as minesweeping. Hypothetically, groups of vehicles may leverage their numbers by cooperating. A fundamental form of cooperation is to perform tasks while maintaining a geometric formation. The formation behavior can then enable other cooperative behaviors. In this paper, we describe a leader-follower formation-flying control algorithm. This algorithm can be applied to one-, two-, and threedimensional formations, and contains a degree of built-in robustness. Simulations and experiments are described that characterize the performance of the formation control algorithm. The experiments utilized surface craft that were equipped with an acoustic navigation and communication system, representative of the technologies that constrain the operation of underwater autonomous vehicles. The simulations likewise included the discrete-time nature of the communication and navigation.

This paper presents a technique for learning to assess terrain traversability for outdoor mobile robot navigation using human-embedded logic and real-time perception of terrain features extracted from image data. The methodology utilizes a fuzzy logic framework and vision algorithms for analysis of the terrain. The terrain assessment and learning methodology is tested and validated with a set of realworld image data acquired by an onboard vision system.

A mathematical model of a valve-regulated lead-acid battery under discharge is presented as simplified from a standard electrodynamics model. This nonlinear reaction-diffusion model of a battery cell is solved using an operator splitting method to quickly and accurately simulate sulfuric acid concentration. This splitting method incorporates one-sided approximation schemes to preserve continuity over material interfaces encompassing discontinuous parameters. Numerical results are compared with measured data by calculating battery voltage from modeled acid concentration as derived from the Nernst equation.

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