Literatura académica sobre el tema "Robots autonomes – Contrôle technique – Catastrophes"

Le sujet de thèse porte sur la reconnaissance automatique de points d’intérêts (PI) pour un robot d’inspection dans un environnement contraint et dégradé. L’objectif de ces travaux de thèse est de développer une plateforme robotique capable d’effectuer des missions en autonomie en se basant sur des PI visuels et chimiques détectés, une problématique dite bimodale. La combinaison des percepts visuels et chimiques permet d’optimiser la précision de localisation etassure une redondance d’information. Le domaine d’étude concerne 3 cas d’application : le cas 1, l’inspection est réalisée dans un espace confiné (milieu industriel). Le cas 2, l’inspection est réalisée dans un environnement avec un risque avéré de perte de signal et à dominante rocheuse (mine, carrière souterraine). Le cas 3, l’inspection est réalisée dans un environnement ayant subi des déformations importantes et donc une géométrie des lieux d’inspection modifiée et chaotique (catastrophes naturelles de type séisme ou éboulement dans un environnement urbain). Dans cette étude, une méthode d’analyse contextuelle des cas est proposée et présentée afin d’analyser les contraintes des différents environnements complexes pour la solution robotique. La thèse regroupe donc différentes problématiques : l’étude des contraintes de l’environnement, le choix de la solution robotique, la navigation autonome et l’asservissement visuel et chimique. Suite à cette analyse contextuelle, un état de l’art est orienté sur la plateforme robotique terrestre pour déterminer la solution robotique la plus adaptée pour opérer dans les 3 cas d’application. Les robots hexapodes ont été choisis pour leurs capacités à franchir les obstacles, leurs stabilités, et leurs capacités d’emport pour les capteurs, notamment. Une méthode est proposée pour atteindre la source du percept dans un environnement non structuré en s’appuyant sur les PI visuels et chimiques. Pour l’évaluation de la méthodologie proposée, les PI visuels considérées sont de type QR code et la détection de la concentration d’un gaz concernant l’asservissement chimique. L’efficacité du schéma proposé est d’abord démontrée par des simulations. Enfin, un prototype d’hexapode est conçu, construit et développé en utilisant l’architecture logicielle ROS. L’hexapode développé a réalisé une mission au sein d’un environnement industriel et à l’intérieur d’une construction navale comprenant une série d’obstacles (cas 1 de l’étude). Les résultats de cette approche robotique sont enfin présentés, commentés et discutés
The thesis subject concerns the automatic recognition of points of interest (PI) for an inspection robot in a constrained and degraded environment. The objective of this thesis work is to develop a robotic platform capable of carrying out autonomous missions based on detected visual and chemical PIs, a so-called bimodal problem. The combination of visual and chemical percepts optimizes localization accuracy and ensures information redundancy. The field of study concerns 3 application cases: case 1, the inspection is carried out in a confined space (industrial environment). Case 2, the inspection is carried out in an environment with a proven risk of loss of signal and predominantly rocky (mine, underground quarry). Case 3, the inspection is carried out in an environment that has undergone significant deformations and therefore a modified and chaotic geometry of the inspection sites (natural disasters such as earthquakes or landslides in an urban environment). In this study, a contextual case analysis method is proposed and presented in order to analyze the constraints of the different complex environments for the robotic solution. The thesis therefore brings together different issues: the study of environmental constraints, the choice of the robotic solution, autonomous navigation and visual and chemical servoing. Following this contextual analysis, a state of the art is oriented on the terrestrial robotic platform to determine the most suitable robotic solution to operate in the 3 application cases. The hexapod robots were chosen for their ability to overcome obstacles, their stability, and their carrying capacity for sensors, in particular. A method is proposed to reach the source of the percept in an unstructured environment by relying on visual and chemical PIs. For the evaluation of the proposed methodology, the visual PIs considered are of the QR code type and the detection of the concentration of a gas concerning chemical servoing. The effectiveness of the proposed scheme is first demonstrated by simulations. Finally, a hexapod prototype is designed, built and developed using the ROS software architecture. The developed hexapod carried out a mission within an industrial environment and inside a shipbuilding including a series of obstacles (case 1 of the study). The results of this robotic approach arefinally presented, commented and discussed

Abstract Traditional techniques to evaluate well's integrity employed for assessing mechanical integrity often come with high operational costs and logistical expenses, as they require the interruption of production or injection processes (1). Consequently, these conventional methods can be impractical and hinder overall efficiency. However, the emergence of autonomous and intelligent intervention systems has paved the way for a significant advancement in this field. These innovative systems possess the capability to make real-time decisions in dynamic and unstructured environments. By using artificial intelligence technologies, it can autonomously evaluate mechanical integrity without human intervention. One of the primary benefits of maintaining mechanical integrity is the assurance of safety and operational efficiency. The integrity of well structures prevents the occurrence of leaks, ruptures, or collapses, which can lead to catastrophic incidents and environmental damage. By employing autonomous and intelligent intervention systems, companies can proactively monitor the mechanical of wells, mitigating potential risks and avoiding costly operational disruptions. These advanced systems are designed to monitor and assess critical parameters such as pressure, temperature, and flow. The real-time decision-making capability of autonomous intervention systems significantly reduces response times to potential integrity threats. By identifying issues, operators can prevent the escalation of problems and minimize associated costs. In addition to improving operational efficiency and safety, autonomous and intelligent intervention systems also offer long-term cost savings. By implementing proactive monitoring strategies, companies can optimize their resource allocation and extend the lifespan of wells. This, in turn, reduces the frequency of costly interventions and enhances overall operational profitability representing a significant leap forward in evaluating well's integrity. By reducing the need for traditional, costly techniques and enabling real-time decision-making equipment, these systems enhance safety, efficiency, and cost-effectiveness. Studies (2, 3) demonstrates that the utilization of RLWI (Rigless Well Intervention) systems can significantly reduce the costs associated with well intervention. Also, the utilization of robotic solutions, as example, wireline tractors have been proved as an excellent solution to improve the efficiency and reliability of well integrity (4, 5, 6). The paper will describe the progress made in developing a modular, autonomous mobile robotic platform for through-tubing operation. We outline the stages of prototype development, current project status, and tests. We also provide details on the artificial intelligence control and autonomous decision-making systems. Finally, we plan to present simulation and field test results in the final section of the paper.

Industry is implementing increasing amounts of automation into operations. The Australian mining industry is no exception as it is introducing autonomous mining vehicles and trains, remote controlled processing plants and the use of drones and robots to do survey and inspection work. Often these technologies are adopted to improve operational efficiencies and to reduce workers' exposure to high risk situations. However, in most mining environments, the adoption of automated technologies has not completely removed humans from the operation. Humans still need to interact with the technology to clean, service and maintain it. Humans also have to perform other tasks in the automated mining environment such as inspection of ground conditions, mapping mining and dump areas, maintaining roads and infrastructure etc. Thus, introducing automation into mining environments has the potential to introduce new and significant human-system interaction safety risks. The emergence of these new safety risks are evident in recent accidents in the mining industry as well as in other industries that have introduced automation. Traditionally, risk based approaches have been used in the Australian mining industry and other industries to identify and treat safety related risks. Such approaches include the use of hazard identification techniques (HAZID), Workplace Risk Assessment and Control (WRAC), Failure Mode and Effects Analysis or Failure Modes and Effects Criticality Analysis (FMEA or FMECA), and Process or Job based Hazard Analysis (PHA or JHA). These traditional techniques have helped reduce fatal and catastrophic incidents in the mining industry but deficiencies in their application has also been highlighted in a number of major accident investigation reports. In addition, recent research has suggested that that traditional risk identification techniques by not be effective for new, software-enabled technologies that are embedded in socio-technical systems with complex or dynamic human-system interactions. In response new socio-technical risk assessment approaches have been develop such as System Theoretic Process Analysis (STPA) and Strategies Analysis for Enhancing Resilience (SAfER). However no publications could be found that seek to understand from a end-user perspective the efficacy of the traditional and new techniques in assessing human-system interaction risks associated with the introduction of autonomous and automated technologies in mining environments.To begin to address this gap, research was conducted that sought to answer the question - What combination of risk assessment techniques delivers the most effective means of identifying risks associated with human-system interactions in remote and autonomous mining operations? The research method involved have mining industry professionals trial four techniques - Preliminary Hazard Analysis (HAZID), Failure Mode and Effects Criticality Analysis (FMECA), Strategies Analysis for Enhancing Resilience (SAfER), and System Theoretic Process Analysis (STPA) (Systems-theory Method) - in a workshop environment. Three different workshops were conducted each of which focused on a different automated technology. The first focused on identifying human-system interaction safety risks in surface mine automated haulage areas. The second focused on identifying human-system interaction safety risk associated with autonomous longwall mining operations underground. The third focused on human-system interaction safety risks associated with remote controlled operation of ore processing plants. After the workshop trialed each technique, the participants were survey to collect their perceptions of the usability and usefulness of each technique. Results from the participant feedback suggest that each techniques was able to identify potentially hazardous human-system interactions but that each had strengths and weaknesses depending on whether risks were being assessed risks pre or post implementation. A hybrid or combination approach was suggested with further testing of the proposed approach being recommended.