Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Computer- and robot-assisted Medical Intervention

Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient. This chapter focuses on such systems. It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the place of robots in that context. The evolutions in terms of general design and control paradigms in the development of medical robots are presented and issues specific to that application domain are discussed. A view of existing systems, on-going developments and future trends is given. A case-study is detailed. Other types of robotic help in the medical environment (such as for assisting a handicapped person, for rehabilitation of a patient or for replacement of some damaged/suppressed limbs or organs) are out of the scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study

Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users

Desarrollo y programación de algoritmos para la evitación automática de colisiones. Aplicación a robots móviles terrestres

[ES] El objetivo de este Trabajo Fin de Máster es diseñar e implementar una metodología y sistema para dotar a un robot terrestre móvil con la capacidad de alcanzar eficientemente una posición objetivo dentro de un entorno limitado y que puede verse condicionado por obstáculos desconocidos. Para cumplir este objetivo, el sistema robótico identifica en primer lugar los obstáculos presentes en el entorno y, a continuación, planifica la mejor trayectoria que permita al robot evitar de forma eficiente cualquier posible colisión, lo cuál es un requisito necesario para su seguridad y completa autonomía. La técnica propuesta aborda la planificación y el despliegue de tales maniobras complejas del robot en un entorno real y son aplicables a cualquier entorno industrial. Esto se logra combinando modelos simples, implementaciones eficientes y simulaciones interactivas que aprovechan la agilidad y la maniobrabilidad del robot. En primer lugar, se obtiene una solucion óptima al problema de la evasión de obstáculos mediante el desarrollo de un controlador en Simulink que se basa en una implementación en Matlab del algoritmo A*, un algoritmo clásico de Inteligencia Artificial que permite calcular trayectorias de coste mínimo desde un punto inicial hasta un objetivo dado en un área delimitada. La estrategia de navegación propuesta se prueba primero con SFunctions para validar interactivamente el comportamiento del controlador, y luego en cosimulación de Simulink y el framework Simscape Multibody, para obtener una visión más real al considerar las propiedades físicas del robot y la fricción. A partir de una matriz de entradas que representa una imagen del entorno obtenida utilizando una cámara monocular, la imagen se segmenta y se procesa utilizando la herramienta software Matlab, y se calcula una trayectoria optima para ir desde un punto inicial a un punto final evadiendo los obstáculos del entorno. La trayectoria calculada se evalúa entonces mediante co-simulación, lo que permite analizar visualmente la ruta estimada seguida por el robot. A continuación, la técnica se implementa en un sistema robótico real, el LEGO MINDOSTORMS EV3, siguiendo una metodología que combina también dos sistemas software diferentes siguiendo dos enfoques complementarios: uno más académico, utilizando la biblioteca de soporte de LEGO MINDSTORMS EV3 para Simulink, que permite evaluar su comportamiento en tiempo real y otro más profesional, utilizando el lenguaje de programación basado en C para róbótica RobotC, que atestigua su aplicabilidad a cualquier sistema industrial.[EN] The goal of this Master Thesis is to design and implement a methodology and system to endow a mobile terrestrial robot with the capability to efficiently reach its goals in a bounded environment that can be constrained with unknown obstacles. To meet this goal, the robotic system first identifies the obstacles in the environment and then plans the best robot trajectory that efficiently avoids any possible collision, which is a necessary requirement for the robot to stay safe and completely autonomous. The technique proposed in this thesis supports planning and deploying of such complex robot maneuvers in a real environment and can be applied to any industrial environment. This is done by combining simple models, efficient implementations, and interactive simulations that leverage the agility and maneuverability of the robot. First, an optimal solution to the problem of obstacle avoidance is achieved by developing in Simulink a controller that relies on a Matlab implementation of the A* algorithm, a classical Artificial Intelligence algorithm that is able to compute minimal cost paths from a start point to a target point in a bounded area. The proposed navigation strategy is first tested with S-Functions to interactively validate the controller¿s behavior, and then in co-simulation of Simulink and the Simscape Multibody framework for a more real view that considers the robot¿s physical properties and friction. Starting from a matrix of input values that correspond to an image of the environment obtained by using a monocular camera, the image is segmented and processed in the Matlab software environment, and an optimal trajectory is computed which leads from the starting point to the target point without collision. The path trajectory is evaluated by using co-simulation to visually analyze the estimated path trajectory followed by the robot. Then, the technique is implemented in a real robotic system, the LEGO MINDOSTORMS EV3, by following a methodology that also combines two different software systems following two complementary approaches: a more academic one, by using LEGO MINDSTORMS EV3 support library for Simulink, which allows the robot real time behavior to be evaluated, and a more professional one, by using the C-based robotics programming language RobotC, which witnesses its applicability to any industrial system.Bahilo Alpuente, P. (2018). Desarrollo y programación de algoritmos para la evitación automática de colisiones. Aplicación a robots móviles terrestres. http://hdl.handle.net/10251/107990TFG