Linear translation motion \(a_x, a_y, a_z\)-acceleration
Magnetometer
Error of integration prevents accurate localisation (known problem in aerospace engineering)
\[a_{x}=\frac{d^2x}{dt^2}\]
the meassurement error is accumulated by double integration of \(a_x\) signal
\[x(t)=\int \int a_{x}d\tau\]
Practical solution is dead-reckoning
The IMU chip is designed to be placed in the geometric center of the ball in order to prevent the error during rotation.
The position of the battery is designed to keep the mass balance of the robot.
Swimbot Design I
Cheaper swimbot (87$)
Large number of swimbot deployments (every batch of transfered fluid should have 2-3 swimbots)
Large number of data for statistical processing and mining
Deep Neural Network
Pipeline integrity and leak prevention
Swimbot Design II
Cheaper swimbot (87$)
Large number of swimbot deployments (every batch of transfered fluid should have 2-3 swimbots)
Large number of data for statistical processing and mining
Deep Neural Network
Big Data Processing
Problem of integration drift is the main issue in all IMUs
Algorithm is applied to fuse acceleration and and angular velocity to generate accurate positioning
Quaternions
Euler angels
Rotation Matrix
Fusion algorithm and Madgwick library
Artificial Neural Network and Deep Neural Network
Model of Deep Neural Network
Experimental Test Environment
Experimental Test Environment
Experimental Results
The experimental run takes 4-5 seconds in the recorded interval between 31 sec and 36 sec
Experimental Results
The experimental run takes 4-5 seconds in the recorded interval between 31 sec and 36 sec
Experimental Results
Position Calculated
Pipe length is 3.22 m and relative error is 5.3%
Acustic data
Summary
Leak localization is explored
Issues of drift associated with IMU are addressed
Fusioon algorithm
Deep Neural Network
Data exploration for large number of deployed swimbots
Future work:
Bayesian inference
Several agent swimbots with communication protocol