Manganese crusts (Mn-crusts) are a type of mineral deposit that exists on the surface of seamounts and guyots at depths of >800 m. We have developed a method to efficiently map their distribution using data collected by autonomous underwater vehicles and remotely operated vehicles. Volumetric measurements of Mn-crusts are made using a high-frequency subsurface sonar and a 3-D visual mapping instrument mounted on these vehicles. We developed an algorithm to estimate Mn-crust distribution by combining continuous subsurface thickness measurements with the exposed surface area identified in 3-D maps. This is applied to data collected from three expeditions at Takuyo Daigo seamount at depths of ∼1400 m. The transects add to ∼11 km in length with 12 510 m 2 mapped. The results show that 52% of the surveyed area is covered by Mn-crusts with a mean thickness of 69.6 mm. The mean Mn-crust occurrence is 69.6 kg/m 2 with a maximum of 204 kg/m 2 in the mapped region. The results are consistent with estimates made from samples retrieved from the area, showing more detailed distribution patterns and having significantly lower uncertainty bounds for regional-scale Mn-crust inventory estimation.

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Multi robot coordination and control for underwater robots is an area of significant importance in many underwater missions. A new approach for leader follower formation control of multi AUV systems is explored in this paper. The controller estimates the next desired position of the follower robot from the past and current positions of the leader and follower robots. The control signals are then issued to the follower robot to align it to the estimated trajectory. This control scheme has the capability to compensate for initial errors and follow the leader under various operational scenarios. The development of the controller and simulation results for selected scenarios are presented. The results show that the proposed method is simple and computationally efficient. where J(η) is called the Jacobian. Neettiyath U. and Thondiyath A..