A framework for telecontrolled service robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present work is a part of the ongoing development of a semi-autonomous remotely controlled IP centric service robot framework.An instance of the framework is a robot for weed extermination in an outdoor environment.Tech- nologies built upon in this thesis include an embedded platform, reconfigurable hardware, a software platform and 802.11WLANI technology for communica- tion.The hardware includes a DC motor contloller subsystem, sensoly sub- system and stepper motor contr-oller subsystem, whereas the software handles the communication between the processor and the remote PC, transforms twodimensional inf'ormation of the joystick to speed and direction commands using a vector-based control scheme and provides the video feedback stream.The robot is comprised of a heavy duty chassis with four wheels.Two DC motors provide the drive for the back wheeis operating from two 12 volts car batteries.Two open source motor control modules (OSMC) implement the high power H-bridge contlol system for each of the motors.This operator assisted robot includes some degree of machine intelligence in order to deal with uncertainly in an outdoor environment.A collision avoidance subsystem provides local intelligence designed to avoid obstacles that the operator may not be able to lespond to quickly enough.This aspect of the framework was based on a fuzzy Iogic controller and utilizes sonar to estimate distances to obstacles.The semi- autonomous operation also includes suitable APIs to implement weed removal selvice tasks with the help of a stepper motor controller subsystem. ACKNOWLEDGVIEI\TSI would like to express my deep and sincere gratitude to my supervisor, Professor Bob VIcLeod for his advice
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
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 it