South American articles presented at the OCEANS Û÷15 conference reflect in a special way how the current productivity in the region is changing interests in science and technology.
By MarÌ_a Victoria Ennis (1)
Luciano Banchio (1)
MarÌ_a Eugenia Conforti (1,2)
Gerardo Gabriel Acosta (1,2)
- Universidad Nacional del Centro de la Provincia de Buenos Aires – (UNICEN), Argentina.
- Consejo Nacional de Investigaciones CientÌ_ficas y TÌ©cnicas (CONICET), Argentina.
Scientific articles written by South American researchers, presented at the last OCEANS conference in Washington, D.C, reflect the region’s current productivity in this discipline.
The growth of the oil industry and the increment of technology investments in South America, mostly driven by national states, have increased the need for better technology and competitive human resources. Researchers have focused on underwater technology application in order to provide prompt solutions to accompany this growth and overcome the difficulties that may prevent the continuity of this boom. Since investments increased, the entire region has been interested and concerned about these matters. This stimulus produced remarkable economical growth for several countries, but also resulted in environmental complications in aquatic ecosystems. These issues were reflected at the OCEANS Ûª15 conference in October, sponsored by the Marine Technology Society (MTS) and IEEE Oceanic Engineering Society (OES).
ROV: How do you drive this?
Underwater ROVs (remotely operated vehicles) represent a key tool in articles presented by South American researchers. To a large extent this is because most of the studied operations are performed offshore, in areas inaccessible to humans.
The first thing to be noted about ROVs is the need to mitigate risks and handling errors. Central to this are quality control and training.
An investigation at the University of Sao Paulo, Brazil, titled “Basic mapping of the inspection process in offshore oil production facilities,Û focuses on monitoring conditions under which the oil and gas underwater devices operate in order to detect and prevent possible failures. The study identifies as key challenges both optimization of cathodic inspection (based on the analysis of the corrosion rate of steel in offshore platforms) and ROV operator training. The researchers propose to map this entire process and create a laboratory at the Polytechnic School of the University of San Pablo, where one the biggest ports in Latin America is located. Qualification of human resources is the No. 1 priority. The authors insist on this point in a second article, “Mobile Control Center: a new approach to deploy ROV-oriented education and training.Û They argue that most procedures related to oil and gas extraction in Brazil are carried out in deepwater by ROVs that require skilled operators.
ROVs are expensive machines that work in extreme conditions, demanding solidly trained operators with a widespread awareness of whole process. The authors state that to address this issue they “discussed a proposal to develop a ROV training center for practical exercises and simulation tests.” Thus, they created the Mobile Control Center (MCC), a training center on a portable base that can be used in the vessels of the university research fleet as well as in marine bases in nearby the town of Santos. A container-based solution was proposed, where the researchers could fit a control center and a winch to interact with different types of ROVs. The MCC has educational purposes and could be potentially applied in on-site research. The authors conclude that “a better work scenario gives the possibility for us to start improving the actual operations instead of worrying about the operators not being able to deliver the expected.”
Nothing moves better than the human mind
Researchers from the Brazilian Institute of Robotics in Salvador, BahÌ_a, presented some technical challenges related to unmanned underwater vehicles (UUVs) used in production operations and research. In a joint study with the Bremen Robotics Innovation Center, “Model Identification of an Unmanned Underwater Vehicle via an Adaptive Technique and Artificial Fiducial Markers,ÛÏ they emphasize the importance of knowing the true trajectory of these vehicles. The UUVs do not travel blindly but with high uncertainty insofar as they move in hostile environments and under variable conditions (ocean currents, high salinity, uneven topography).
Brazilian and German researchers have studied the best method to obtain a dynamic model of the vehicles in order to control their trajectories more efficiently. Conclusions have not provided simplistic answers. They noted that of the four models they tested, none works perfectly in all circumstances: Some are better than others in some aspects. Therefore, they have proposed a superior method particularly effective to model the motion of an autonomous underwater vehicle (AUV). They are building an AUV in Brazil called FlatFish, which does not require human intervention via remote control. The modeling technique they propose combines data coming from two different sources of measurement: fused underwater sensors and AprilTag artificial fiducial markers, detected by a video camera.
For researchers at the National University of Central Buenos Aires in Argentina, there is an even better way to model the motion of the AUVs and better control their trajectories: by copying the way humans learn. Engineers who exposed this idea were inspired by behavioral psychology. This kind of learning involves repeating the actions that give successful results and abandoning those that are unsuccessful.
“The proposed approach enables real-time adaptation”, the researchers state in their article titled “Trajectory tracking algorithm for autonomous vehicles using adaptive reinforcement learning.Û They question the classical control techniques that require a known model of system dynamics, arguing that “when the system of interest operates under so complex and uncertain environments the dynamics modeling becomes very cumbersome or even impossible. Many times, in this type of environments the presence of human operators in the control loops becomes essential.”
Online learning provides data reflected on an algorithm that is updated constantly during the operation. Artificial intelligence provides a control monitoring strategy capable of adapting to unpredictable environments and represents a breakthrough to processes that require vehicles that can make autonomous decisions.
Noise, garbage, turbidity and odor. That is what water receives with every human intrusion, especially when it comes to industrial operations. The article presented by researchers from Pontifical Catholic University of Peru, “ROV-based acquisition system for water quality measuring,Û highlights this problem while offering a system for monitoring water quality in rivers, lakes and oceans aboard a ROV.
The robotic platform integrates a video camera, multi-parameter probe for water quality analysis and an array of three hydrophones to measure underwater noise. “Any kind of energy induced in underwater scenarios affects the environmentÛ_ Air-guns can emit intense acoustic waves in seismic prospection in the search for resources in the oil and gas industry,” the authors note. The ROV functionality was tested with optimal results in a pool that was 3 meters in diameter and 1.5 meters deep. åÊThe next goal, they state, is to test the ROV in natural underwater scenarios and validate the results to be used in surveys by the Peruvian Institute of the Sea (IMARPE).
South American articles presented at the OCEANS Û÷15 conference reflect in a special way how the current productivity in the region is changing interests in science and technology. Facing the production boom in oil and gas, universities and institutes in South America are contributing additional and improved technology, well-trained human resources and new environmental solutions.
See also: EarthzineÛªs coverage of OCEANS Û÷15