Mobile robots

As technology progresses, tasks involving object manipulation that were once conducted by humans are now being accomplished through robots. Specifically, robots carry out these goals through the utilization of different forms of artificial intelligence, including deep learning via a convolutional neural network. One robot made to accomplish this purpose is the ROS controlled TurtleBot3 Waffle Pi with an OpenMANIPULATOR-X robotic arm. This type of TurtleBot3 was developed with the express purpose of education and research but may not be limited to those two usages. Based on the current design of this classification of TurtleBot3, it may have multiple applications outside the testing environment, granting it further uses in a variety of tasks. The TurtleBot3 is easy to setup to fulfill the purposes for which the TurtleBot3 Waffle Pi was designed, and the exploration into further uses would allow for the discovery of alternatives to some tasks that normally require more work. For that reason, this thesis was conducted to determine the various uses of the TurtleBot3 with a robotic arm and if this robot can be used outside of a testing environment for various real-world tasks.

The overall objective of this work is to evaluate the ability of homing and docking an unmanned underwater vehicle (Hydroid REMUS 100 UUV) to a moving unmanned surface vehicle (Wave-Adaptive Modular Surface Vehicle USV) using a Hydroid Digital Ultra-Short Baseline (DUSBL) acoustic positioning system (APS), as a primary navigation source. An understanding of how the UUV can rendezvous with a stationary USV first is presented, then followed by a moving USV. Inherently, the DUSBL-APS is susceptible to error due to the physical phenomena of the underwater acoustic channel (e.g. ambient noise, attenuation and ray refraction). The development of an APS model has allowed the authors to forecast the UUV’s position and the estimated track line of the USV as determined by the DUSBL acoustic sensor. In this model, focus is placed on three main elements: 1) the acoustic channel and sound ray refraction when propagating in an in-homogeneous medium; 2) the detection component of an ideal DUSBL-APS using the Neyman-Pearson criterion; 3) the signal-to-noise ratio (SNR) and receiver directivity impact on position estimation. The simulation tool is compared against actual open water homing results in terms of the estimated source position between the simulated and the actual USBL range and bearing information.