This paper proposes a hierarchical image-based visual servoing (IBVS) strategy for dynamic positioning of a fully actuated underwater vehicle. In the kinematic loop, the desired velocity is generated by a nonlinear model predictive controller, which optimizes a cost function of the predicted image trajectories under the constraints of visibility and velocity. A velocity reference model, representing the desired closed-loop vehicle dynamics, is integrated with an IBVS kinematic model to predict the future trajectories. In the dynamic velocity tracking loop, a neural-network-based model reference adaptive controller is designed to ensure the convergence of the velocity tracking error in the presence of uncertainties associated with vehicle dynamic parameters, water velocity, and thrust forces. Comparative simulations with different control and system configurations are performed to verify the effectiveness of the proposed scheme and to illustrate the influences of the prediction horizon, cost function, closed-loop vehicle dynamics, and predictive velocity reference model on the IBVS system performance.

No abstract

Many onboard navigation systems use the Global Positioning System to bound the errors that result from integrating inertial sensors over time. Global Positioning System information, however, is not always accessible since it relies on external satellite signals. To this end, a vision sensor is explored as an alternative for inertial navigation in the context of an Extended Kalman Filter used in the closed-loop control of an unmanned aerial vehicle. The filter employs an onboard image processor that uses camera images to provide information about the size and position of a known target, thereby allowing the flight computer to derive the target's pose. Assuming that the position and orientation of the target are known a priori, vehicle position and attitude can be determined from the fusion of this information with inertial and heading measurements. Simulation and flight test results verify filter performance in the closed-loop control of an unmanned rotorcraft.

No abstract

No abstract

No abstract

The methods of data collection, processing, and assessment of the quality of the results of a survey conducted at the Southern Ionian Sea off the Messinian Peninsula, Greece are presented. Data were collected by the GEBCO-Nippon Foundation Alumni Team, competing in the Shell Ocean Discovery XPRIZE, during the Final Round of the competition. Data acquisition was conducted by the means of unmanned vehicles only. The mapping system was composed of a single deep water AUV (Autonomous Underwater Vehicle), equipped with a high-resolution synthetic aperture sonar HISAS 1032 and multibeam echosounder EM 2040, partnered with a USV (Unmanned Surface Vessel). The USV provided positioning data as well as mapping the seafloor from the surface, using a hull-mounted multibeam echosounder EM 304. Bathymetry and imagery data were collected for 24 h and then processed for 48 h, with the extensive use of cloud technology and automatic data processing. Finally, all datasets were combined to generate a 5-m resolution bathymetric surface, as an example of the deep-water mapping capabilities of the unmanned vehicles' cooperation and their sensors' integration.The ocean floor is the most remote and difficult part of our planet, yet to be explored. Due to the extreme conditions facing exploration, the majority of the world's ocean floor is still unmapped [1,2]. Modern mapping techniques, based on the acoustic technology, allow us the possibility to collect bathymetric and water column data, but the survey process is slow and expensive. The majority of present-day surveying efforts go into the busier, shallow waters for the purpose of safety of navigation. While this represents an understandable and necessary approach, it results in the majority of the deep ocean still being unmapped. The overall shape of the global ocean floor is an estimation based on the satellite altimetry technique [3] and interpolation between sparse acoustic measurements.The American foundation XPRIZE announced in 2015 that the next award will go to those who come up with the solution for faster, detailed mapping of the ocean floor, without exposing humans to the harsh sea environment. That was the beginning of the Shell Ocean Discovery XPRIZE. To compete in the final round of the Shell Ocean Discovery XPRIZE, each team had to present the solution for collecting bathymetric data and imagery data and successfully complete Round 1 in 2017. During the second round a year later, the bathymetric data collection had to achieve depths of up to 4000 m, with a vertical resolution of 0.5 m and a horizontal resolution of 5 m. The imagery needed to include images presenting recognizable geological, biological, or archaeological features on the seabed. The solution had to be autonomous or remote controlled, but there must have been no human inside the survey area, and no physical link between the devices and the mission control location. The mapping system, with all its elements, had to fit into one standard 40-ft shipping container. The minimum coverage to be achieved during t...

No abstract