Robotics in Dentistry : A Narrative Review

Background: Robotics is progressing rapidly. The aim of this study was to provide a comprehensive overview of the basic and applied research status of robotics in dentistry and discusses its development and application prospects in several major professional fields of dentistry. Methods: A literature search was conducted on databases: MEDLINE, IEEE and Cochrane Library, using MeSH terms: [“robotics” and “dentistry”]. Result: Forty-nine articles were eventually selected according to certain inclusion criteria. There were 12 studies on prosthodontics, reaching 24%; 11 studies were on dental implantology, accounting for 23%. Scholars from China published the most articles, followed by Japan and the United States. The number of articles published between 2011 and 2015 was the largest. Conclusions: With the advancement of science and technology, the applications of robots in dental medicine has promoted the development of intelligent, precise, and minimally invasive dental treatments. Currently, robots are used in basic and applied research in various specialized fields of dentistry. Automatic tooth-crown-preparation robots, tooth-arrangement robots, drilling robots, and orthodontic archwire-bending robots that meet clinical requirements have been developed. We believe that in the near future, robots will change the existing dental treatment model and guide new directions for further development

Learning about tooth removal with robot technology

Deze PhD-thesis richt zich op een fundamenteel onderzoek van de extractieleer en maakt daarbij gebruik van robottechnologie. Het onderzoek omvat zes inhoudelijke hoofdstukken, waarin verschillende aspecten van dit onderwerp worden behandeld. Het tweede hoofdstuk analyseert de literatuur over robottechnologie in de tandheelkunde en wijst op de matige kwaliteit van beschikbare literatuur, zeker als het op klinische toepassingen aankomt. Hoofdstuk 3 biedt een overzicht van robotsystemen in alle deelgebieden van de tandheelkunde sinds 1985. Hoofdstuk 4 introduceert een meetopstelling om krachten en bewegingen bij tandextracties nauwkeurig vast te leggen, terwijl hoofdstuk 5 de resultaten van een serie experimenten voor wat betreft krachten en momenten weergeeft. Hoofdstuk 6 beschrijft het bewegingsbereik en de snelheden tijdens tandheelkundige extracties, zoals gemeten met een robotarm. Hoofdstuk 7 beschrijft de ontwikkeling en eigenschappen van een classificatiemodel voor extracties op basis van kracht- en bewegingsgegevens. De conclusie benadrukt de toenemende interesse in robotinitiatieven in de tandheelkunde, de behoefte aan wetenschappelijke validatie van de toegevoegde waarde daarvan en het potentieel van robottechnologie om ons fundamentele begrip van de extractieleer te vergroten. De studies benadrukken het belang van gegevensverzameling, analyse en samenwerking tussen verschillende disciplines om ons fundamentele begrip van extracties te verbeteren, met een focus op tandheelkundig onderwijs en uiteindelijk de patiëntenzorg

Ultraviolet disinfection (UV-D) robots: bridging the gaps in dentistry

Maintaining a microbe-free environment in healthcare facilities has become increasingly crucial for minimizing virus transmission, especially in the wake of recent epidemics like COVID-19. To meet the urgent need for ongoing sterilization, autonomous ultraviolet disinfection (UV-D) robots have emerged as vital tools. These robots are gaining popularity due to their automated nature, cost advantages, and ability to instantly disinfect rooms and workspaces without relying on human labor. Integrating disinfection robots into medical facilities reduces infection risk, lowers conventional cleaning costs, and instills greater confidence in patient safety. However, UV-D robots should complement rather than replace routine manual cleaning. To optimize the functionality of UV-D robots in medical settings, additional hospital and device design modifications are necessary to address visibility challenges. Achieving seamless integration requires more technical advancements and clinical investigations across various institutions. This mini-review presents an overview of advanced applications that demand disinfection, highlighting their limitations and challenges. Despite their potential, little comprehensive research has been conducted on the sterilizing impact of disinfection robots in the dental industry. By serving as a starting point for future research, this review aims to bridge the gaps in knowledge and identify unresolved issues. Our objective is to provide an extensive guide to UV-D robots, encompassing design requirements, technological breakthroughs, and in-depth use in healthcare and dentistry facilities. Understanding the capabilities and limitations of UV-D robots will aid in harnessing their potential to revolutionize infection control practices in the medical and dental fields

Learning Shape Control of Elastoplastic Deformable Linear Objects

Deformable object manipulation tasks have long been regarded as challenging robotic problems. However, until recently very little work has been done on the subject, with most robotic manipulation methods being developed for rigid objects. Deformable objects are more difficult to model and simulate, which has limited the use of model-free Reinforcement Learning (RL) strategies, due to their need for large amounts of data that can only be satisfied in simulation. This paper proposes a new shape control task for Deformable Linear Objects (DLOs). More notably, we present the first study on the effects of elastoplastic properties on this type of problem. Objects with elastoplasticity such as metal wires, are found in various applications and are challenging to manipulate due to their nonlinear behavior. We first highlight the challenges of solving such a manipulation task from an RL perspective, particularly in defining the reward. Then, based on concepts from differential geometry, we propose an intrinsic shape representation using discrete curvature and torsion. Finally, we show through an empirical study that in order to successfully solve the proposed task using Deep Deterministic Policy Gradient (DDPG), the reward needs to include intrinsic information about the shape of the DLO

Additively Manufactured Polymer and Metal Lattice Structures with Eulerian Path

Lattice structure manufacturing with polymers and metals can benefit from the use of Eulerian paths. In this research, two types of lattice fabrication methods are studied where the Eulerian path can be applicable. Polymer lattice is improved by using a new assembly design, while a new way of metal lattice fabrication is discussed. For the fused filament fabrication process, a new interlocking design and assemble-based lattice structure building approach is investigated by increasing continuity in layers and avoiding support structures. To minimize contour plurality, Eulerian paths between the edges were enforced. Two configurations in the form of cubic and octet lattice structures are examined. The compressive performance of the designed lattice structures is compared with the traditional single-build direct 3D printed lattice structures. The mechanical performance (e.g., peak stress, specific energy absorption) of the assembled structures is found to be generally better than their direct print counterparts. The empirical constants of the Ashby-Gibson power law are found to be larger than their suggested values in both direct print and assembly techniques. However, their values are more compliant for octet assembled structures, which are less susceptible to manufacturing imperfections. A novel method of metal lattice manufacturing is introduced where a straight wire is bent to make intermediate structures, they are stacked, and loose nodes are joined to get the final lattice. The limitations of this method are studied, and a machine is constructed that can overcome some of the limitations and produce fabricable and stackable structures. These structures, generated by a custom-made visual basic code, can be periodic or aperiodic using a function to optimize the topology. The transient liquid phase (TLP) diffusion bonding method is studied as an appropriate joining method due to the inaccessibility of the nodes after stacking

Issues in Contemporary Orthodontics

Issues in Contemporary Orthodontics is a contribution to the ongoing debate in orthodontics, a discipline of continuous evolution, drawing from new technology and collective experience, to better meet the needs of students, residents, and practitioners of orthodontics. The book provides a comprehensive view of the major issues in orthodontics that have featured in recent debates. Abroad variety of topics is covered, including the impact of malocclusion, risk management and treatment, and innovation in orthodontics

Robot Learning for Manipulation of Deformable Linear Objects

Deformable Object Manipulation (DOM) is a challenging problem in robotics. Until recently there has been limited research on the subject, with most robotic manipulation methods being developed for rigid objects. Part of the challenge in DOM is that non-rigid objects require solutions capable of generalizing to changes in shape and mechanical properties. Recently, Machine Learning (ML) has been proven successful in other fields where generalization is important such as computer vision, thus encouraging the application of ML to robotics as well. Notably, Reinforcement Learning (RL) has shown promise in finding control policies for manipulation of rigid objects. However, RL requires large amounts of data that are better satisfied in simulation while deformable objects are inherently more difficult to model and simulate. This thesis presents ReForm, a simulation sandbox for robotic manipulation of Deformable Linear Objects (DLOs) such as cables, ropes, and wires. DLO manipulation is an interesting problem for a variety of applications throughout manufacturing, agriculture, and medicine. Currently, this sandbox includes six shape control tasks, which are classified as explicit when a precise shape is to be achieved, or implicit when the deformation is just a consequence of a more abstract goal, e.g. wrapping a DLO around another object. The proposed simulation environments aim to facilitate comparison and reproducibility of robot learning research. To that end, an RL algorithm is tested on each simulated task providing initial benchmarking results. ReForm is one of three concurrent frameworks to first support DOM problems. This thesis also addresses the problem of DLO state representation for an explicit shape control problem. Moreover, the effects of elastoplastic properties on the RL reward definition are investigated. From a control perspective, DLOs with these properties are particularly challenging to manipulate due to their nonlinear behavior, acting elastic up to a yield point after which they become permanently deformed. A low-dimensional representation from discrete differential geometry is proposed, offering more descriptive shape information than a simple point-cloud while avoiding the need for curve fitting. Empirical results show that this representation leads to a better goal description in the presence of elastoplasticity, preventing the RL algorithm from converging to local minima which correspond to incorrect shapes of the DLO

A Textbook of Advanced Oral and Maxillofacial Surgery

The scope of OMF surgery has expanded; encompassing treatment of diseases, disorders, defects and injuries of the head, face, jaws and oral cavity. This internationally-recognized specialty is evolving with advancements in technology and instrumentation. Specialists of this discipline treat patients with impacted teeth, facial pain, misaligned jaws, facial trauma, oral cancer, cysts and tumors; they also perform facial cosmetic surgery and place dental implants. The contents of this volume essentially complements the volume 1; with chapters that cover both basic and advanced concepts on complex topics in oral and maxillofacial surgery

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way