A stability and control system for a hexapod underwater robot
Bibliographic record
Abstract
Aqua is an underwater hexapod robot that uses paddles to propel and orient itself.The system is highly non-linear and coupled, and thus far a controller has not been implemented on the robot.In this work, three different controllers were developed and utilized on the robot.The design of a controller for the vehicle began with the development of a stability augmentation system (SAS).In order to study the stability of the system, the model needed to be linearized and this was accomplished using numerical differentiation by finite differences.Using this method state space matrices were derived for three different steady state velocities and corresponding SAS's were designed based on the system's eigenvalues.These SAS's were implemented in a nonlinear simulation and were shown to need further refinement.The refined SAS's were then designed and were successfully implemented on the physical robot in fresh and sea water.The design of an autopilot to operate with the SAS followed which included a proportional and a proportional-integral controller.The controllers were tested in simulation and in experiment with inconsistent results.Finally, the SAS was modified to compensate for possible faults that may occur during the operation of the robot.It was found that the original SAS was sufficiently robust to compensate for the case of a missing flipper.However, the case of a flipper stuck at a fixed angle required a modification to the SAS and this was accomplished by analyzing the additional drag forces created by the fault.The modified SAS was implemented on the robot in a set of experiments with successful results.John-Paul Lobos, Alec Mills, and Yogesh Girdhar for their help during the field trials; and Ioannis Rekleitis for helping me with L A T E X and his scuba diving advice.Finally, I would like to thank my friends and family for their continual support and encouragement.Thanks go to my parents, Clement
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".