Useful computer vision techniques for human-robot interaction

[ES]This paper describes some simple but useful computer vision techniques for human-robot interaction. First, an omnidirectional camera setting is described that can detect people in the surroundings of the robot, giving their angular positions and a rough estimate of the distance. The device can be easily built with inexpensive components. Second, we comment on a color-based face detection technique that can alleviate skin-color false positives. Third, a simple head nod and shake detector is described, suitable for detecting affirmative/negative, approval/dissaproval, understanding/disbelief head gestures.

Installazione del navigation stack su rover terrestre e applicazione del kinect nello human-robot interaction.

Progetto SHERPA. Installazione e configurazione del Navigaton Stack su Rover terrestre. Utilizzo e configurazione di LMS151 Sick. Utilizzo e configurazione di Asus Xtion Pro. Progettazione di software per la localizzazione e l'inseguimento di persone tramite camera di profondita.

Human-robot interaction for telemanipulation in large workspaces

Independientemente de la existencia de técnicas altamente sofisticadas y capacidades de cómputo cada vez más elevadas, los problemas asociados a los robots que interactúan con entornos no estructurados siguen siendo un desafío abierto en robótica. A pesar de los grandes avances de los sistemas robóticos autónomos, hay algunas situaciones en las que una persona en el bucle sigue siendo necesaria. Ejemplos de esto son, tareas en entornos de fusión nuclear, misiones espaciales, operaciones submarinas y cirugía robótica. Esta necesidad se debe a que las tecnologías actuales no pueden realizar de forma fiable y autónoma cualquier tipo de tarea. Esta tesis presenta métodos para la teleoperación de robots abarcando distintos niveles de abstracción que van desde el control supervisado, en el que un operador da instrucciones de alto nivel en la forma de acciones, hasta el control bilateral, donde los comandos toman la forma de señales de control de bajo nivel. En primer lugar, se presenta un enfoque para llevar a cabo la teleoperación supervisada de robots humanoides. El objetivo es controlar robots terrestres capaces de ejecutar tareas complejas en entornos de búsqueda y rescate utilizando enlaces de comunicación limitados. Esta propuesta incorpora comportamientos autónomos que el operador puede utilizar para realizar tareas de navegación y manipulación mientras se permite cubrir grandes áreas de entornos remotos diseñados para el acceso de personas. Los resultados experimentales demuestran la eficacia de los métodos propuestos. En segundo lugar, se investiga el uso de dispositivos rentables para telemanipulación guiada. Se presenta una aplicación que involucra un robot humanoide bimanual y un traje de captura de movimiento basado en sensores inerciales. En esta aplicación, se estudian las capacidades de adaptación introducidas por el factor humano y cómo estas pueden compensar la falta de sistemas robóticos de alta precisión. Este trabajo es el resultado de una colaboración entre investigadores del Biorobotics Laboratory de la Universidad de Harvard y el Centro de Automática y Robótica UPM-CSIC. En tercer lugar, se presenta un nuevo controlador háptico que combina velocidad y posición. Este controlador bilateral híbrido hace frente a los problemas relacionados con la teleoperación de un robot esclavo con un gran espacio de trabajo usando un dispositivo háptico pequeño como maestro. Se pueden cubrir amplias áreas de trabajo al cambiar automáticamente entre los modos de control de velocidad y posición. Este controlador háptico es ideal para sistemas maestro-esclavo con cinemáticas diferentes, donde los comandos se transmiten en el espacio de la tarea del entorno remoto. El método es validado para realizar telemanipulación hábil de objetos con un robot industrial. Por último, se introducen dos contribuciones en el campo de la manipulación robótica. Por un lado, se presenta un nuevo algoritmo de cinemática inversa, llamado método iterativo de desacoplamiento cinemático. Este método se ha desarrollado para resolver el problema cinemático inverso de un tipo de robot de seis grados de libertad donde una solución cerrada no está disponible. La eficacia del método se compara con métodos numéricos convencionales. Además, se ha diseñado una taxonomía robusta de agarres que permite controlar diferentes manos robóticas utilizando una correspondencia, basada en gestos, entre los espacios de trabajo de la mano humana y de la mano robótica. El gesto de la mano humana se identifica mediante la lectura de los movimientos relativos del índice, el pulgar y el dedo medio del usuario durante las primeras etapas del agarre. ABSTRACT Regardless of the availability of highly sophisticated techniques and ever increasing computing capabilities, the problems associated with robots interacting with unstructured environments remains an open challenge. Despite great advances in autonomous robotics, there are some situations where a humanin- the-loop is still required, such as, nuclear, space, subsea and robotic surgery operations. This is because the current technologies cannot reliably perform all kinds of task autonomously. This thesis presents methods for robot teleoperation strategies at different levels of abstraction ranging from supervisory control, where the operator gives high-level task actions, to bilateral teleoperation, where the commands take the form of low-level control inputs. These strategies contribute to improve the current human-robot interfaces specially in the case of slave robots deployed at large workspaces. First, an approach to perform supervisory teleoperation of humanoid robots is presented. The goal is to control ground robots capable of executing complex tasks in disaster relief environments under constrained communication links. This proposal incorporates autonomous behaviors that the operator can use to perform navigation and manipulation tasks which allow covering large human engineered areas of the remote environment. The experimental results demonstrate the efficiency of the proposed methods. Second, the use of cost-effective devices for guided telemanipulation is investigated. A case study involving a bimanual humanoid robot and an Inertial Measurement Unit (IMU) Motion Capture (MoCap) suit is introduced. Herein, it is corroborated how the adaptation capabilities offered by the human-in-the-loop factor can compensate for the lack of high-precision robotic systems. This work is the result of collaboration between researchers from the Harvard Biorobotics Laboratory and the Centre for Automation and Robotics UPM-CSIC. Thirdly, a new haptic rate-position controller is presented. This hybrid bilateral controller copes with the problems related to the teleoperation of a slave robot with large workspace using a small haptic device as master. Large workspaces can be covered by automatically switching between rate and position control modes. This haptic controller is ideal to couple kinematic dissimilar master-slave systems where the commands are transmitted in the task space of the remote environment. The method is validated to perform dexterous telemanipulation of objects with a robotic manipulator. Finally, two contributions for robotic manipulation are introduced. First, a new algorithm, the Iterative Kinematic Decoupling method, is presented. It is a numeric method developed to solve the Inverse Kinematics (IK) problem of a type of six-DoF robotic arms where a close-form solution is not available. The effectiveness of this IK method is compared against conventional numerical methods. Second, a robust grasp mapping has been conceived. It allows to control a wide range of different robotic hands using a gesture based correspondence between the human hand space and the robotic hand space. The human hand gesture is identified by reading the relative movements of the index, thumb and middle fingers of the user during the early stages of grasping.

Experimental tests in human-robot collision evaluation and characterization of a new safety index for robot operation

This paper describes an experimental procedure consisting of impact tests that simulate a collision of a human head with an industrial robot with the aim to validate a safety index named as New Index for Robots (NIR) and its outputs. The experiments in this paper are based on lab tests. It is an attempt to characterize the NIR index underlying the main parameters that are involved in crash interaction and to highlight limitations and weakness of suggested impact tests.

Achieving cooperation in a distributed multi-robot team

DMAPS (Distributed Multi-Agent Planning System) is a planning system developed for distributed multi-robot teams based on MAPS(Multi-Agent Planning System). MAPS assumes that each agent has the same global view of the environment in order to determine the most suitable actions. This assumption fails when perception is local to the agents: each agent has only a partial and unique view of the environment. DMAPS addresses this problem by creating a probabilistic global view on each agent by fusing the perceptual information from each robot. The experimental results on consuming tasks show that while the probabilistic global view is not identical on each robot, the shared view is still effective in increasing performance of the team.

Fast and accurate hand pose detection for human-robot interaction

[EN]Enabling natural human-robot interaction using computer vision based applications requires fast and accurate hand detection. However, previous works in this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects difficult to locate. This paper presents an approach which integrates temporal coherence cues and hand detection based on wrists using a cascade classifier. With this approach, we introduce three main contributions: (1) a transparent initialization mechanism without user participation for segmenting hands independently of their gesture, (2) a larger number of detected gestures as well as a faster training phase than previous cascade classifier based methods and (3) near real-time performance for hand pose detection in video streams.

Computational Foundations for Safe and Efficient Human-Robot Collaboration in Assembly Cells

Human and robots have complementary strengths in performing assembly operations. Humans are very good at perception tasks in unstructured environments. They are able to recognize and locate a part from a box of miscellaneous parts. They are also very good at complex manipulation in tight spaces. The sensory characteristics of the humans, motor abilities, knowledge and skills give the humans the ability to react to unexpected situations and resolve problems quickly. In contrast, robots are very good at pick and place operations and highly repeatable in placement tasks. Robots can perform tasks at high speeds and still maintain precision in their operations. Robots can also operate for long periods of times. Robots are also very good at applying high forces and torques. Typically, robots are used in mass production. Small batch and custom production operations predominantly use manual labor. The high labor cost is making it difficult for small and medium manufacturers to remain cost competitive in high wage markets. These manufactures are mainly involved in small batch and custom production. They need to find a way to reduce the labor cost in assembly operations. Purely robotic cells will not be able to provide them the necessary flexibility. Creating hybrid cells where humans and robots can collaborate in close physical proximities is a potential solution. The underlying idea behind such cells is to decompose assembly operations into tasks such that humans and robots can collaborate by performing sub-tasks that are suitable for them. Realizing hybrid cells that enable effective human and robot collaboration is challenging. This dissertation addresses the following three computational issues involved in developing and utilizing hybrid assembly cells: - We should be able to automatically generate plans to operate hybrid assembly cells to ensure efficient cell operation. This requires generating feasible assembly sequences and instructions for robots and human operators, respectively. Automated planning poses the following two challenges. First, generating operation plans for complex assemblies is challenging. The complexity can come due to the combinatorial explosion caused by the size of the assembly or the complex paths needed to perform the assembly. Second, generating feasible plans requires accounting for robot and human motion constraints. The first objective of the dissertation is to develop the underlying computational foundations for automatically generating plans for the operation of hybrid cells. It addresses both assembly complexity and motion constraints issues. - The collaboration between humans and robots in the assembly cell will only be practical if human safety can be ensured during the assembly tasks that require collaboration between humans and robots. The second objective of the dissertation is to evaluate different options for real-time monitoring of the state of human operator with respect to the robot and develop strategies for taking appropriate measures to ensure human safety when the planned move by the robot may compromise the safety of the human operator. In order to be competitive in the market, the developed solution will have to include considerations about cost without significantly compromising quality. - In the envisioned hybrid cell, we will be relying on human operators to bring the part into the cell. If the human operator makes an error in selecting the part or fails to place it correctly, the robot will be unable to correctly perform the task assigned to it. If the error goes undetected, it can lead to a defective product and inefficiencies in the cell operation. The reason for human error can be either confusion due to poor quality instructions or human operator not paying adequate attention to the instructions. In order to ensure smooth and error-free operation of the cell, we will need to monitor the state of the assembly operations in the cell. The third objective of the dissertation is to identify and track parts in the cell and automatically generate instructions for taking corrective actions if a human operator deviates from the selected plan. Potential corrective actions may involve re-planning if it is possible to continue assembly from the current state. Corrective actions may also involve issuing warning and generating instructions to undo the current task.

Off-world robotic excavation for Large-scale habitat construction and resource extraction

This paper describes technologies we have developed to perform autonomous large-scale off-world excavation. A scale dragline excavator of size similar to that required for lunar excavation was made capable of autonomous control. Systems have been put in place to allow remote operation of the machine from anywhere in the world. Algorithms have been developed for complete autonomous digging and dumping of material taking into account machine and terrain constraints and regolith variability. Experimental results are presented showing the ability to autonomously excavate and move large amounts of regolith and accurately place it at a specified location.

Grounding human vocabulary in robot perception through interaction

This thesis addresses the problem of word learning in computational agents. The motivation behind this work lies in the need to support language-based communication between service robots and their human users, as well as grounded reasoning using symbols relevant for the assigned tasks. The research focuses on the problem of grounding human vocabulary in robotic agent’s sensori-motor perception. Words have to be grounded in bodily experiences, which emphasizes the role of appropriate embodiments. On the other hand, language is a cultural product created and acquired through social interactions. This emphasizes the role of society as a source of linguistic input. Taking these aspects into account, an experimental scenario is set up where a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. The agent grounds the names of these objects in visual perception. Word learning is an open-ended problem. Therefore, the learning architecture of the agent will have to be able to acquire words and categories in an openended manner. In this work, four learning architectures were designed that can be used by robotic agents for long-term and open-ended word and category acquisition. The learning methods used in these architectures are designed for incrementally scaling-up to larger sets of words and categories. A novel experimental evaluation methodology, that takes into account the openended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. The results indicate that all approaches were able to incrementally acquire new words and categories. Although some of the approaches could not scale-up to larger vocabularies, one approach was shown to learn up to 293 categories, with potential for learning many more.

RGBD Human-Hand recognition for the Interaction with Robot-Hand

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS.

Probabilistic world modeling for distributed team planning

This paper describes an application of decoupled probabilistic world modeling to achieve team planning. The research is based on the principle that the action selection mechanism of a member in a robot team can select an effective action if a global world model is available to all team members. In the real world, the sensors are imprecise, and are individual to each robot, hence providing each robot a partial and unique view about the environment. We address this problem by creating a probabilistic global view on each agent by combining the perceptual information from each robot. This probabilistic view forms the basis for selecting actions to achieve the team goal in a dynamic environment. Experiments have been carried out to investigate the effectiveness of this principle using custom-built robots for real world performance, in addition, to extensive simulation results. The results show an improvement in team effectiveness when using probabilistic world modeling based on perception sharing for team planning.

Global reconfiguration of a team of networked mobile robots among obstacles

This paper presents a full system demonstration of dynamic sensorbased reconfiguration of a networked robot team. Robots sense obstacles in their environment locally and dynamically adapt their global geometric configuration to conform to an abstract goal shape. We present a novel two-layer planning and control algorithm for team reconfiguration that is decentralised and assumes local (neighbour-to-neighbour) communication only. The approach is designed to be resource-efficient and we show experiments using a team of nine mobile robots with modest computation, communication, and sensing. The robots use acoustic beacons for localisation and can sense obstacles in their local neighbourhood using IR sensors. Our results demonstrate globally-specified reconfiguration from local information in a real robot network, and highlight limitations of standard mesh networks in implementing decentralised algorithms.