Deployment Of Robot Arms With A Leader-Follower Approach For Bomb Disposal
Bibliographic record
Abstract
Improvised Explosive Devices (IEDs) have been developed over the years across many nations around the world. IEDs used by terrorist actions and in warfare cause devastating death, injuries and damage. To protect the public, many emergency responders have to risk their lives by performing extremely hazardous tasks such as interacting with suspected IEDs. To prevent the emergency response teams from being negatively impacted by IEDs, many different kinds of response robots have been deployed in many locations worldwide – allowing first responders a safe way to interact with these menaces from a distance. This thesis contributes to the understanding of using robot arms with a Leader–Follower (LF) approach to help humans with performing dexterous operations like those which are inevitably required for manipulating IEDs remotely. The LF approach allows operators to remotely manipulate a robot arm without putting operators’ lives in danger. By physically controlling one arm from a safe distance, operators can successfully copy its movements to a second arm. As a result, we argue, this approach can be helpful for minimizing operator risk when interacting with suspicious devices while at the same time facilitating more intuitive remote control.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".