Teleoperation Assist System for All-terrain Mobile Manipulator in Narrow and Rough Environment
Bibliographic record
Abstract
In order to perform surveillance missions in case of natural/human-caused disasters, all-terrain mobile manipulators are useful tools for rescue crews' safety. It has a capability to traverse on rough terrain, and to handle objects with the mounted manipulator. For example, the mobile manipulator “Packbot” opened a door in Fukushima Daiichi Nuclear accident in 2011. However, it is well-known that it requires a lot of skill for its teleoperation, particularly in case of missions in narrow and rough terrain. Based on our ex-researches, we found the following issues: (1) According to the rough terrain, the pose of the manipulator is not fitted with the inertial frame of reference, and it prevents an intuitive teleoperation. (2) In narrow areas, the manipulator contacts with the environment because of the lack of environmental information. (3) Communication delay makes more difficult for teleoperation. To solve the above issues, in this research, we implemented a base-altitude synchronous type master-slave controller for the issue (1), teleoperation system with vision and 3D information for the issue (2), and anti-communication-delay-system with 3D point cloud information for the issue (3). To evaluate the above system, we conducted some experiments with non-skilled operators. In this paper, we describe the above system implementation, and report the experimental results to evaluate the above system.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".