A framework for telecontrolled service robots
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\TS I would like to express my deep and sincere gratitude to my supervisor,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".