The Augmentation of Urban Search and Rescue Dogs With Sensing, Control, and Actuation--Extending the Metaphor, "Dog as Robot"
Notice bibliographique
Résumé
When disaster strikes in urban areas, the devastating results are collapsed structures that may contain voids, and trapped people within. To a large extent, the speed with which these victims can be found and extricated determines the likelihood of their survival. Specially trained and equipped emergency first responders are tasked with trying to save their lives by locating and extricating trapped victims from these dangerous environments. Telepresence systems can help first responders search for casualties from a safe location. Most automated search systems intended for use in urban disasters, come in the form of remotely operated robots. This work takes a different approach to telepresence and robotics. This work is an extension of previous work that exploits the intelligence and characteristics of trained search dogs combined with compatible technology and used as components in new kinds of telepresence systems for urban search and rescue (USAR) operations. The Canine Remote Deployment System (CRDS) is a tool that emergency responders can use to deliver critical supplies to trapped victims in rubble using dogs. The first contribution of this work is the development of the bark detection system for automatically triggering deployment of packages near trapped victims from the CRDS-guaranteeing accurate package deployment even when remote communication with the dog is impossible. A well-known ground robot problem is the difficulty in designing a mobility mechanism to traverse rubble. Another contribution of this thesis is the Canine Assisted Robot Deployment (CARD) framework and the design of a robot capable of being carried by a search dog. This work extends the responder’s telepresence in rescue operations by bringing robots much deeper into the disaster site than current methods. Visual odometry is used in location tracking in GPS-denied environments and can be used in rescue operations. This research explores the limitation of RGB-D cameras for visual odometry for this application. An algorithm called pseudo-Random Interest Points Extractor was developed iv to track images over visually feature-sparse areas with the potential use of visually reconstructing canine search paths to victims. This work concentrates on using visual odometry from data collected from a search dog-mounted RGB-D camera. The task of model stabilization is difficult due to the nature of dog’s constant and unpredictable movements, asthe data contains many motion blurred images. The development of an algorithm called Intelligent Frame Selector is shown to improve visual odometry for systems carried by search dogs by intelligently filtering data and selecting only usable frames. The algorithm can be applied to any general visual odometry pipeline beneficially as the technique reduces cumulative error problems by using less data.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».