Preliminary Design, Modelling, and Motion Planning of a Robotic Fastening System for Aircraft Wing Interior Assembly
Bibliographic record
Abstract
This thesis focuses on the development of a proof-of-concept design, modelling, and motion planning for a mobile snake robot for aircraft wing box assembly. For design, several concepts have been explored against the requirement that the robot would move from station to station and mimic a human arm to reach inside the wing box through an access hole to install fasteners. The final design is a P1R4 snake robot along with an end effector socket allowing for alignment compliance when the tool engages with a fastener. For modeling, forward kinematics is formulated using the DH method and verified with a PoE approach. An analytical solution for inverse kinematics is found. For motion planning, first path planning is carried out from the robot locking position to the entrance point of the access hole, and then entering inside the wing box to reach the desired fastening target locations. Second, trajectory generation is realized using MATLAB ppval function and collision detection is performed using MATLAB inShape function, which generates a natural cubic spline interpolation from a given set of waypoints prescribed from the wing box CAD data and ensures no pose collisions by using MATLAB cylinder2P and alphaShape functions. The planned path is verified through simulation using MATLAB Simscape. The case study simulation results show that the snake robot can access about 95.4% of the interior of the wing box to perform the required fastening operation. In conclusion, this thesis work has demonstrated the feasibility of the proposed method.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".