Icebergs originating from high latitude glaciers have drawn much attention from scientists
and offshore operators in the North Atlantic. Scientists are curious about the
iceberg drift and deterioration, while the offshore industry is concerned about the
potential risks and damages on offshore oil platforms and infrastructures. In order to
provide information to improve the iceberg drift and deterioration model constructed
by scientists, and to assess the threats posed by icebergs to offshore platforms, iceberg
shapes need to be measured. For the above water portion, optical instruments
such as a camera and a laser scanner/LIDAR can be used. However, measuring the
underwater portion of an iceberg is more challenging due to navigational constraints
and sensor limitations. One approach, commonly used, is to deploy a horizontal plane
scanning sonar from a support vessel at several locations around the iceberg. There
are many drawbacks to this method, including the cost, sensing trade-offs in resolution
and coverage, as well as constraints because of weather conditions limiting safe
operations.
The technology of Autonomous Underwater Vehicles (AUVs) has been developing
rapidly in the last two decades. AUVs are commonly chosen to carry scientific sensors
for various oceanographic applications. Without human intervention, AUVs can accomplish
pre-programmed missions autonomously and deliver scientific data upon the
users’ request. With these advantages, AUVs are considered as potential candidates in underwater iceberg sensing operations because they can operate close to icebergs
to measure shapes and collect environmental data of the surrounding water. Sonar is
usually used for underwater mapping applications. Since AUVs are typically quieter
acoustically than manned surface vessels, a low noise to signal ratio can be achieved
on sonars carried by AUVs.
In this research, a technology of AUV-based underwater iceberg-profiling is evaluated.
An iceberg-profiling simulator is constructed to analyse underwater iceberg-profiling
missions. With the simulator, the accuracy of AUV-based operation is compared
with conventional methods of deploying sonar profilers around icebergs. Beyond the
simulation, a guidance, navigation, and control (GNC) system is designed with an
objective of guiding the vehicle traveling around the iceberg at a standoff distance.
The GNC uses measurements from a mechanical scanning sonar to construct a vehicleattached
occupancy map (VOM) that the probability of occupancy of the cells in the
VOM is updated based on a dynamic inverse-sonar model. Using the occupancy
information about the cells in the VOM, the line-of-sight (LOS) guidance law is used
to compute the desired heading for the existing heading controller in the AUV. The
GNC is first calibrated and validated in a simulated environment. Then, an AUV
equipped with a forward side-looking mechanical scanning sonar is deployed in the
field. The GNC guides the vehicle circumnavigated an iceberg autonomously, and
underwater shape of the target iceberg is represented using the sonar samples.
The point cloud may deviate from the original iceberg shape due to the iceberg movement.
A motion estimation algorithm is developed to estimate the iceberg motion
for converting the point cloud into an iceberg-centered coordinate system. Two point
clouds measured at different times, inputs of the motion estimation algorithm, are
presumed to be identical in the iceberg-centered coordinate system. Then, the algorithm
iteratively updates the motion estimates based on the translational matrix and rotational matrix from an iterative closest point (ICP) algorithm to match the point
clouds. The hypothesis that two point clouds are identical in the iceberg-centered
coordinate system is valid when the motion estimates are converged in the updating
process. Once the iceberg motion is resolved, the point cloud in the inertial coordinate
can be converted in to the iceberg-centered coordinate to present the true iceberg
shape. The algorithm for estimating iceberg motion is applied to data collected from
the simulation environment and the field trials in Newfoundland