Design Improvement, Implementation, and Numerical Modeling of a Self-Adaptive Perching Tool for UAVs
Bibliographic record
Abstract
RÉSUMÉ: Le Module de Fixation de Base (BAM de l'anglais) est un outil robotique utilisant des ventouses pneumatiques, combinées à des articulations mécaniques permettant de fixer de petits véhicules aériens sans pilote (UAV de l'anglais) sur des surfaces d'intérêt. L'UAV, équipé pour effectuer un type de travail mécanique, peut ainsi déployer ses outils tout en étant solidement accroché à la surface et réaliser le travail en comptant sur le BAM pour fournir la résistance nécessaire aux forces et aux couples résultant du travail effectué. Dans le but de fournir autant de polyvalence que possible concernant les surfaces d'exploitation et le type de travail à effectuer, le BAM est censé s'adapter à des surfaces de textures et de formes variables, se fixer relativement facilement malgré une précision limitée de la part du contrôleur de vol, et rester en place suffisamment longtemps pour effectuer le travail souhaité. Pour ce faire, ce travail aborde les lacunes de conception des prototypes précédents du BAM, propose et teste des améliorations dans une nouvelle version du prototype, puis approfondit l'analyse de la mobilité de la nouvelle version et propose des améliorations potentielles supplémentaires. Le processus suit une approche d'ingénierie des systèmes qui commence par l'identification des exigences des utilisateurs (en l'occurrence, le Conseil National de Recherches du Canada) et les transforme en exigences du système. Cela est suivi par la conception, l'approvisionnement ou la fabrication, l'assemblage et les tests des différentes unités et sous-systèmes. Ensuite, les différentes unités et sous-systèmes sont intégrés dans le système complet, et validés avec des tests en laboratoire et en vol sur diverses surfaces de perchage d'intérêt. Avec la livraison d'une preuve de concept validée, des analyses supplémentaires de la mobilité du système sont effectuées en utilisant la théorie des visseurs, et des améliorations sont proposées pour remédier à certaines des lacunes restantes liées aux degrés de liberté du mécanisme. ABSTRACT: The Base Attachment Module (BAM) is a spatial robotic tool that uses pneumatic suction cups, combined with active and passive joints, to attach small uncrewed aerial vehicles (UAVs) to surfaces of interest. The UAV, equipped to perform some type of mechanical work, can therefore deploy its tools while safely perched on the surface, relying on the BAM to provide the necessary resistance to the forces and torques resulting from the work performed. Aiming to provide as much versatility as possible regarding the operating surfaces and the type of work to be performed, the BAM is expected to adapt to surfaces of varying texture and shape, attach with relative ease despite limited precision on the flight controller's part, and remain perched long enough to complete the desired work. To that end, this work addresses the shortcomings of previous designs and prototypes of the BAM, proposes and tests improvements to the design in a new version of the prototype, and then delves deeper into the analysis of the mobility of the modified version and proposes additional potential improvements. The process follows a systems engineering approach that starts with identifying the user requirements (the user being the National Research Council of Canada) and transforming them into system requirements. This is followed by designing, procuring or manufacturing, assembling, and testing the various units and subsystems. The various units and subsystems are then integrated into the complete system and validated with in-flight laboratory tests on various perching surfaces of interest. With the delivery of a validated proof of concept, additional analyses of the system's mobility are performed using screw theory, and enhancements are proposed to address some of the remaining shortcomings related to the mechanism's degrees of freedom.
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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 | 0.001 |
| 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) | 0.003 | 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".