Control Algorithm of an Autonomous Obstacles Negotiating Inspection Robot for Power Transmission Lines
Bibliographic record
Abstract
In biorobotics research, engineers and biologists come together to implement the researcher's vision of the mechanisms driving a biological process in steel and silicon. Bionics has made a great progress and appeared all kinds of biologic design and application for robot. This paper describes the development of a mobile robot capable of negotiation such obstacles as electric power fitting, damper, anchor clamp, and torsion tower. The mobile robot suspends on overhead ground wires of 500KV power towers. Its ultimate purpose is to automate the inspection of power transmission line equipment. Biology principle of the movement of the monkey is taken as reference prototype to design and produce the inspection robot driven by 13 motor with two arms, two wheels, two wrists, two claws and a box. The inspection robot is designed to realize the function of observation, grasp, walk, rolling, turn, rise, and decline. An embedded computer based on PC/104 bus is chosen as the core of control system. A video capture card and thermal infrared camera are installed to obtain the temperature information of the power transmission lines, and the communication system between the robot and the ground station is based on wireless LAN TCP/IP protocol. An expert system programmed with Visual C++ is developed to implement the automatic control. A novel prototype with careful considerations on mobility was designed to inspect the 500KV power transmission lines. The new control algorithm of posture plan is proposed for obstacles cleaning in the torsion tower. Results of experiments demonstrate that the robot can be applied to execute the navigation and inspection tasks.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".