An interactive approach of assembly planning

An interactive approach to assembly planning is presented. It provides a virtual reality interface for production engineers to program the virtual representation of robotic manipulators in a three-dimensional (3D) operation space. The direct human involvement creates a user-defined assembly sequence, which contains the human knowledge of mechanical assembly. By extracting the precedence relationship of machinery parts, for the first time it becomes possible to generate alternative assembly sequences automatically from a single sequence for robot reprogramming. This interactive approach introduces human expertise into assembly planning, thus breaking down the computational complexity of autonomous systems. Experiments and analysis provide strong evidence to support the incontestable advantages of manufacturing in the computer

Assembly Guidance in Augmented Reality Environments Using a Virtual Interactive Tool

The application of augmented reality (AR) technology for assembly guidance is a novel approach in the traditional manufacturing domain. In this paper, we propose an AR approach for assembly guidance using a virtual interactive tool that is intuitive and easy to use. The virtual interactive tool, termed the Virtual Interaction Panel (VirIP), involves two tasks: the design of the VirIPs and the real-time tracking of an interaction pen using a Restricted Coulomb Energy (RCE) neural network. The VirIP includes virtual buttons, which have meaningful assembly information that can be activated by an interaction pen during the assembly process. A visual assembly tree structure (VATS) is used for information management and assembly instructions retrieval in this AR environment. VATS is a hierarchical tree structure that can be easily maintained via a visual interface. This paper describes a typical scenario for assembly guidance using VirIP and VATS. The main characteristic of the proposed AR system is the intuitive way in which an assembly operator can easily step through a pre-defined assembly plan/sequence without the need of any sensor schemes or markers attached on the assembly components.Singapore-MIT Alliance (SMA

Survey on assembly sequencing: a combinatorial and geometrical perspective

A systematic overview on the subject of assembly sequencing is presented. Sequencing lies at the core of assembly planning, and variants include finding a feasible sequence—respecting the precedence constraints between the assembly operations—, or determining an optimal one according to one or several operational criteria. The different ways of representing the space of feasible assembly sequences are described, as well as the search and optimization algorithms that can be used. Geometry plays a fundamental role in devising the precedence constraints between assembly operations, and this is the subject of the second part of the survey, which treats also motion in contact in the context of the actual performance of assembly operations.Peer ReviewedPostprint (author’s final draft

Programming by demonstration of the sequence of tightening a nut allowing variations in tool position

Se presenta una técnica que permite la programación por demostración de un robot para que ejecute una tarea secuencial o compleja. Se utiliza una combinación de redes de Petri y modelos de mezcla de gaussianas parametrizado en la tarea; con la primera se coordina la secuencia de la tarea, en tanto que la segunda permite variaciones en la posición y orientación de los objetos de la misma. Una técnica de segmentación de tareas, descompone la demostración en subtareas. Con la secuencia de las subtareas, se obtiene una lista de acciones (plan) y con este se genera de manera automática una red de Petri. A la técnica también se le suministran las plantillas modelo de cada subtarea y los modelos de mezcla de gaussianas parametrizados en la tarea de las trayectorias de la subtarea que se quiere que admita variaciones. Una función compara las trayectorias de cada plantilla con las trayectorias repuesta del modelo, y la de mayor similitud indica que en vez de la plantilla, se debe emplear el modelo de mezcla parametrizado. Mediante el uso de un robot de fabricación propia, el cual ejecuta la tarea de tomar, transportar una llave y apretar una tuerca, se ilustra el desempeño de la técnica a través de gráficas.A technique of programming by demonstration of a robot is proposed. Such a technique allows that a robot execute sequential or complex tasks. It uses a combination of Petri nets and task parameterized Gaussian mixture models. The first one handles the task sequence, while the second one allows variations in the position and orientation of objects involved in the task. Using a segmentation task technique, the demonstration is chunked in subtasks. With the subtasks sequence, an action list or plan is obtained and with this, a Petri net is automatically generate. Models of the templates of each subtasks and task parameterized Gaussian mixture models of the subtask that we want to allow variations are also provide to the technique. A function compare one each of the template trajectory with the task parameterized model response trajectory and the most similar indicate that instead of the template, the task parameterized model is use. Through the use of a homemade robot, which executes the task of tightening a nut, the performance of the technique is illustrated by using figures

On-Orbit Manoeuvring Using Superquadric Potential Fields

On-orbit manoeuvring represents an essential process in many space missions such as orbital assembly, servicing and reconfiguration. A new methodology, based on the potential field method along with superquadric repulsive potentials, is discussed in this thesis. The methodology allows motion in a cluttered environment by combining translation and rotation in order to avoid collisions. This combination reduces the manoeuvring cost and duration, while allowing collision avoidance through combinations of rotation and translation. Different attractive potential fields are discussed: parabolic, conic, and a new hyperbolic potential. The superquadric model is used to represent the repulsive potential with several enhancements. These enhancements are: accuracy of separation distance estimation, modifying the model to be suitable for moving obstacles, and adding the effect of obstacle rotation through quaternions. Adding dynamic parameters such as object translational velocity and angular velocity to the potential field can lead to unbounded actuator control force. This problem is overcome in this thesis through combining parabolic and conic functions to form an attractive potential or through using a hyperbolic function. The global stability and convergence of the solution is guaranteed through the appropriate choice of the control laws based on Lyapunov's theorem. Several on-orbit manoeuvring problems are then conducted such as on-orbit assembly using impulsive and continuous strategies, structure disassembly and reconfiguration and free-flyer manoeuvring near a space station. Such examples demonstrate the accuracy and robustness of the method for on-orbit motion planning

Aportes en la Generalización de Habilidades en Aprendizaje por Imitación de Robots

En programación por demostración (PpD) de robots, las variaciones de posición de los objetos relacionados con una tarea, requieren nuevas trayectorias que respondan a estas. Una de las técnicas existentes, es el modelo de mezcla de gaussianas parametrizado en la tarea. Este modelo permite relacionar los movimientos del robot con metas y poses de objetos, los cuales son los llamados parámetros de la tarea. Un problema que aparece es que se deben generalizar las trayectorias tanto en el espacio cartesiano, como en el de articulación, más específicamente, se requiere tener la cinemática inversa del robot, con el cual se puedan estimar las trayectorias de articulación, a partir de las trayectorias cartesianas. Un segundo problema que se presenta cuando se manejan objetos deformables, es que se pueden presentar fallos de ejecución, lo que requiere de una o varias acciones de recuperación. Un tercer problema es que, aunque las técnicas de generalización responden ante cambios, en ciertas ocasiones es necesario incluir nuevos comportamientos, los cuales pueden ser diferentes a los ya aprendidos. Este trabajo, se centra en tres aportes relacionados con generalización de trayectorias: i) El aprendizaje y aplicación en PpD de la cinemática directa con una red neuronal llamada máquina de aprendizaje extremo y con la cual se estima la cinemática inversa; ii) La recuperación ante fallos de ejecución en tareas empleando múltiples modelos de mezcla de gaussianas parametrizados en la tarea, y iii) El aprendizaje incremental de trayectorias novedosas en modelos de mezcla de gaussianas parametrizados en la tarea. El funcionamiento de las técnicas propuestas fue probado a través de simulaciones y experimentos con robots reales. La máquina de aprendizaje extremo, aunque requiere un buen número de datos para estimar la cinemática directa, presenta un error bajo cuando se compara con el obtenido por transformaciones homogeneas. Para las propuestas de recuperación a fallos y aprendizaje incremental, se evaluó la tarea de colocar una manga a un maniquí con un manipulador robótico. En la técnica de recuperación de fallos se encontró que la técnica propuesta mejora la realización de la tarea en la mayoría de los casos; y en el aprendizaje incremental, el nuevo modelo parametrizado obtenido después del incremento, presenta mejores respuestas que las logradas empleando el modelo existenteAbstract : In robot programming by demonstration (PbD) object positions changes related to a task, requires new trajectories that respond to these. One of the existent technique is the task parametrized Gaussian mixture model. This technique allows to relate the robot movements with goals and objects poses, which are called task parameters. One problem that emerge is the need of generalization in cartesian and joint space, specifically it is required to have the direct kinematic model of the robot, with which it is possible to estimate the joints trajectories from cartesian ones. A second problem that arises is that when manipulate deformable object, it is possible to have execution fails, it requires the execution of one of more actions to recovery the fail. A third problem is that, although the generalization technique responds to changes, in certain occasions, is necessary to include new behaviors, which can be different from those already learned. This work focuses on three contributions related to trajectory generalization issue: i) The learning and application of the direct kinematics, in PbD using a neural network called extreme learning machine; ii) The recovery of execution fails, in tasks programming with multiple task parametrized gaussian mixture models, and iii) The incremental learning of novelty trajectory, in task parametrized gaussian mixture models. The proposed techniques were tested through simulations and experiments with real robots. Although the extreme learning machine requires a big number of data to estimate the kinematics, it has a low error, when comparing it with the obtained from homogeneus transforms. For the proposed techniques in fail recovery and incremental learning, the task of putting a sleeve to a mannequin with a robotic manipulator was evaluated. In fail recovery, was found that the technique improving the task performance in most cases; and in the incremental learning, the new task parameterized model obtained after the increase, showed better performance than that of the existent modelDoctorad

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