MSc Robotics Final Project by Johanna Amunyela

Investigating using the EKF  The  Extended Kalman Filter  (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear.  Reference video description :   This video composes three synchronized views at the beginning and also shows the 3D reconstruction of the structure at the end. The top left corner shows the real operation of the robot moving around the structure gathering the 3D scans. The bottom left corner shows the generated map with each scan painted with a different color. There it can be seen how the point clouds are added to the map with the estimation of the robot position (i.e. with the pose predicted by the EKF) producing inconsistencies and that, after the registration results are incorporated to the EKF the map consiste...

The new drilling process will contain three Machine Learning Models. These models work together to make the mining decisions required to mine accurately and efficiently. Improvements  The plan is to replace the current Sonar with a solid-state lidar sensor that produces point cloud data. The Current R2 Sonic 2024 Sonar produces real-time results but has too much acoustic fluctuation and might benefit from a stabilising approach with Graph Convolutional Neural Network   references  The finding from this Underwater mobile mapping video is very inciteful. comparing the traditional acoustic sonar approach, with the point cloud lidar approach.

Observations I notice the model is working well but not performing as intended so I will look at the data and hyperparameters to see what is going on. I will also remove the winch data. This is because the tag/sensor data for the vessel witches are not as consistent and it mostly flagged 0 which means no movement on the winches and this is not helpful. In the near future, when the data is more accurate the winch tag/sensor data will help clearly train for pre-move. Pre-move  is when the vessel moves while the drill is down to save time moving to the next hole.

After waiting some time, I received the 3D data for the panel that was mined on the days I selected to train the model against. I intend to train two models, one with geological 3d data and the other with drill bit data. The plan is that they will work together to achieve the desired outcome or to find an algorithm that will consider both data types and produce the binary classification with a distance in meters of how much to pick up the drill bit by. This will ultimately save time because you will not need to pull up the bit to the default 8 meters knowing the geology or lithology of the sea floor. Research into GCN (Graph Convolutional Networks ) I have also found this video that speaks about GCN. The content encourages using dynamic graph CNN for learning on point clouds, in our case the .xyz file. it also advised considering the points in a point cloud data as nodes in a directed graph. these models help learn from non- euclidean data like graphs and 3d objects using a different ...

I manage to install the drill visualisation software and received a bit of training on how to navigate it. The ultimate objective of the drill visualisation is to use the prediction data from the flask API to visualise the drill up and down composition in replay mode. this is purely experimental and might not yield the expected result since we haven't used 3d data this far when building the model.

 Looking at the data the previous model was not looking at the correct fields. after some investigation, I found 56 rows of data in the 460085 rows of data that did not have the binary value, causing errors and incorrect values.  below are the Sklearn model. I made a comparison between 3 algorithms  Decision Tree Classifier 99.5 % Logistic Regression  96.5% KNeighbors Classifier 98.2% The Model that performed better was the Decision tree classifier see image below: