Modeling of Complex Parts for Industrial WaterJet Cleaning

Industrial high-pressure waterjet cleaning is common to many industries. The modeling in this paper functions inside a collaborative robotic framework for high mix, low volume processes where human robot collaboration is beneficial. Automation of pressure washing is desirable for economic and ergonomic reasons. An automated cleaning system needs path simulation and analysis to give the operator insight into the predicted cleaning performance of the system. In this paper, ablation, the removal of a substrate coating by waterjet, is modeled for robotic cleaning operations. The model is designed to work with complex parts often found in spray cleaning operations, namely parts containing hidden portions, holes, or concavities. Experimentation is used to validate and calibrate the ablation model to yield accurate evaluations for how well every feature of a part is cleaned based on the cumulative effect of water affecting the part surface. The ablation model will provide the foundation for optimizing process parameters for robotic waterjet cleaning

Collaborative Robotic Path Planning for Industrial Spraying Operations on Complex Geometries

Implementation of automated robotic solutions for complex tasks currently faces a few major hurdles. For instance, lack of effective sensing and task variability – especially in high-mix/low-volume processes – creates too much uncertainty to reliably hard-code a robotic work cell. Current collaborative frameworks generally focus on integrating the sensing required for a physically collaborative implementation. While this paradigm has proven effective for mitigating uncertainty by mixing human cognitive function and fine motor skills with robotic strength and repeatability, there are many instances where physical interaction is impractical but human reasoning and task knowledge is still needed. The proposed framework consists of key modules such as a path planner, path simulator, and result simulator. An integrated user interface facilitates the operator to interact with these modules and edit the path plan before ultimately approving the task for automatic execution by a manipulator that need not be collaborative. Application of the collaborative framework is illustrated for a pressure washing task in a remanufacturing environment that requires one-off path planning for each part. The framework can also be applied to various other tasks, such as spray-painting, sandblasting, deburring, grinding, and shot peening. Specifically, automated path planning for industrial spraying operations offers the potential to automate surface preparation and coating in such environments. Autonomous spray path planners in the literature have been limited to generally continuous and convex surfaces, which is not true of most real parts. There is a need for planners that consistently handle concavities and discontinuities, such as sharp corners, holes, protrusions or other surface abnormalities when building a path. The path planner uses a slicing-based method to generate path trajectories. It identifies and quantifies the importance of concavities and surface abnormalities and whether they should be considered in the path plan by comparing the true part geometry to the convex hull path. If necessary, the path is then adapted by adjusting the movement speed or offset distance at individual points along the path. Which adaptive method is more effective and the trade-offs associated with adapting the path are also considered in the development of the path planner

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Learning Task Models for Robotic Manipulation of Nonrigid Objects

As robots become more prevalent in smaller manufacturing and maintenance settings, it will become important to enable them to learn new tasks quickly without explicit programming by a human. One particularly challenging domain in robot learning is handling nonrigid objects and materials such as fluids and easily deformable parts and tools. The complexities of modeling nonrigid systems make it infeasible in general for a robot to plan its actions to perform a task by simulating their behavior, requiring an ability to learn an unknown model through experience. This experience can be gained both from a human demonstrating the way to perform a task and the robot itself performing task attempts to incrementally improve its model. Over time, as more experience is acquired, the robot should eventually obtain a model that allows it to perform the task when faced with new variations, generalizing its past experience. This dissertation explores this problem in the context of two robot tasks: pouring a specific volume of fluid into a moving container, and cleaning stains off of compliant objects. First, an approach is presented to learn the parameters of the pouring task by observing human demonstrations. The model learned from the demonstrations can then be exploited to learn how to pour new volumes with minimal extra learning effort by the robot. Second, this same task is used in development of a general approach for autonomous learning. Here, the robot takes a small set of random samples from the parameter space to build an initial task model and selects new parameters to test by building many local linear models. As more data is acquired, the robot's task performance improves substantially and it is able to very quickly find solutions to new task variations. Then another approach is shown that uses demonstrations to estimate a cost function for performing the task. This enables the robot to also learn strategy elements from how humans perform tasks. Finally, two approaches are discussed to learn the deformation model of a compliant part. A bimanual setup with two robot arms is used to hold and clean the part and the model is used to optimize the plans for both arms to reduce cleaning time and deformations. The first approach shows a black-box learning method to directly predict the part deformation when a known force is applied. The second uses a finite-element structure to represent the part, and learns the model by updating the stiffness parameters. When given a new part, the system only needs a few trials to improve quickly enough to clean new stains efficiently by predicting how much the part will deform under cleaning force

Adaptive Control For Autonomous Navigation Of Mobile Robots Considering Time Delay And Uncertainty

Autonomous control of mobile robots has attracted considerable attention of researchers in the areas of robotics and autonomous systems during the past decades. One of the goals in the field of mobile robotics is development of platforms that robustly operate in given, partially unknown, or unpredictable environments and offer desired services to humans. Autonomous mobile robots need to be equipped with effective, robust and/or adaptive, navigation control systems. In spite of enormous reported work on autonomous navigation control systems for mobile robots, achieving the goal above is still an open problem. Robustness and reliability of the controlled system can always be improved. The fundamental issues affecting the stability of the control systems include the undesired nonlinear effects introduced by actuator saturation, time delay in the controlled system, and uncertainty in the model. This research work develops robustly stabilizing control systems by investigating and addressing such nonlinear effects through analytical, simulations, and experiments. The control systems are designed to meet specified transient and steady-state specifications. The systems used for this research are ground (Dr Robot X80SV) and aerial (Parrot AR.Drone 2.0) mobile robots. Firstly, an effective autonomous navigation control system is developed for X80SV using logic control by combining ‘go-to-goal’, ‘avoid-obstacle’, and ‘follow-wall’ controllers. A MATLAB robot simulator is developed to implement this control algorithm and experiments are conducted in a typical office environment. The next stage of the research develops an autonomous position (x, y, and z) and attitude (roll, pitch, and yaw) controllers for a quadrotor, and PD-feedback control is used to achieve stabilization. The quadrotor’s nonlinear dynamics and kinematics are implemented using MATLAB S-function to generate the state output. Secondly, the white-box and black-box approaches are used to obtain a linearized second-order altitude models for the quadrotor, AR.Drone 2.0. Proportional (P), pole placement or proportional plus velocity (PV), linear quadratic regulator (LQR), and model reference adaptive control (MRAC) controllers are designed and validated through simulations using MATLAB/Simulink. Control input saturation and time delay in the controlled systems are also studied. MATLAB graphical user interface (GUI) and Simulink programs are developed to implement the controllers on the drone. Thirdly, the time delay in the drone’s control system is estimated using analytical and experimental methods. In the experimental approach, the transient properties of the experimental altitude responses are compared to those of simulated responses. The analytical approach makes use of the Lambert W function to obtain analytical solutions of scalar first-order delay differential equations (DDEs). A time-delayed P-feedback control system (retarded type) is used in estimating the time delay. Then an improved system performance is obtained by incorporating the estimated time delay in the design of the PV control system (neutral type) and PV-MRAC control system. Furthermore, the stability of a parametric perturbed linear time-invariant (LTI) retarded type system is studied. This is done by analytically calculating the stability radius of the system. Simulation of the control system is conducted to confirm the stability. This robust control design and uncertainty analysis are conducted for first-order and second-order quadrotor models. Lastly, the robustly designed PV and PV-MRAC control systems are used to autonomously track multiple waypoints. Also, the robustness of the PV-MRAC controller is tested against a baseline PV controller using the payload capability of the drone. It is shown that the PV-MRAC offers several benefits over the fixed-gain approach of the PV controller. The adaptive control is found to offer enhanced robustness to the payload fluctuations

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Generating timed trajectories foran autonomous robot

Tese de Doutoramento Programa Doutoral em Engenharia Electrónica e ComputadoresThe inclusion of timed movements in control architectures for mobile navigation has received an increasing attention over the last years. Timed movements allow modulat- ing the behavior of the mobile robot according to the elapsed time, such that the robot reaches a goal location within a specified time constraint. If the robot takes longer than expected to reach the goal location, its linear velocity is increased for compen- sating the delay. Timed movements are also relevant when sequences of missions are considered. The robot should follow the predefined time schedule, so that the next mission is initiated without delay. The performance of the architecture that controls the robot can be validated through simulations and field experiments. However, ex- perimental tests do not cover all the possible solutions. These should be guided by a stability analysis, which might provide directions to improve the architecture design in cases of inadequate performance of the architecture. This thesis aims at developing a navigation architecture and its stability analysis based on the Contraction Theory. The architecture is based on nonlinear dynamical systems and must guide a mobile robot, such that it reaches a goal location within a time constraint while avoiding unexpected obstacles in a cluttered and dynamic real environment. The stability analysis based on the Contraction Theory might provide conditions to the dynamical systems parameters, such that the dynamical systems are designed as contracting, ensuring the global exponential stability of the architecture. Furthermore, Contraction Theory provides solutions to analyze the success of the mis- sion as a stability problem. This provides formal results that evaluate the performance of the architecture, allowing the comparison to other navigation architectures. To verify the ability of the architecture to guide the mobile robot, several experi- mental tests were conducted. The obtained results show that the proposed architecture is able to drive mobile robots with timed movements in indoor environments for large distances without human intervention. Furthermore, the results show that the Con- traction Theory is an important tool to design stable control architectures and to analyze the success of the robotic missions as a stability problem.A inclusão de movimentos temporizados em arquitecturas de controlo para navegação móvel tem aumentado ao longo dos últimos anos. Movimentos temporizados permitem modular o comportamento do robô de tal forma que ele chegue ao seu destino dentro de um tempo especificado. Se o robô se atrasar, a sua velocidade linear deve ser aumen- tada para compensar o atraso. Estes movimentos são também importantes quando se consideram sequências de missões. O robô deve seguir o escalonamento da sequência, de tal forma que a próxima missão seja iniciada sem atraso. O desempenho da arqui- tectura pode ser validado através de simulações e experiências reais. Contudo, testes experimentais não cobrem todas as possíveis soluções. Estes devem ser conduzidos por uma análise de estabilidade, que pode fornecer direcções para melhorar o desempenho da arquitectura. O objectivo desta tese é desenvolver uma arquitectura de navegação e analisar a sua estabilidade através da teoria da Contracção. A arquitectura é baseada em sistemas dinâmicos não lineares e deve controlar o robô móvel num ambiente real, desordenado e dinâmico, de tal modo que ele chegue à posição alvo dentro de uma restrição de tempo especificada. A análise de estabilidade baseada na teoria da Contracção pode fornecer condições aos parâmetros dos sistemas dinâmicos de modo a desenha-los como contracções, e assim garantir a estabilidade exponencial global da arquitectura. Esta teoria fornece ainda soluções interessantes para analisar o sucesso da missão como um problema de estabilidade. Isto providencia resultados formais que avaliam o desem- penho da arquitectura e permitem a comparação com outras arquitecturas. Para verificar a habilidade da arquitectura em controlar o robô móvel, foram con- duzidos vários testes experimentais. Os resultados obtidos mostram que a arquitectura proposta é capaz de controlar robôs móveis com movimentos temporizados em ambi- entes interiores durante grandes distâncias e sem intervenção humana. Além disso, os resultados mostram que a teoria da Contracção é uma ferramenta importante para desenhar arquitecturas de controlo estáveis e para analisar o sucesso das missões efec- tuadas pelo robô como um problema de estabilidade.Portuguese Science and Technology Foundation (FCT) SFRH/BD/68805/2010

PLANNING FOR AUTOMATED OPTICAL MICROMANIPULATION OF BIOLOGICAL CELLS

Optical tweezers (OT) can be viewed as a robot that uses a highly focused laser beam for precise manipulation of biological objects and dielectric beads at micro-scale. Using holographic optical tweezers (HOT) multiple optical traps can be created to allow several operations in parallel. Moreover, due to the non-contact nature of manipulation OT can be potentially integrated with other manipulation techniques (e.g. microfluidics, acoustics, magnetics etc.) to ensure its high throughput. However, biological manipulation using OT suffers from two serious drawbacks: (1) slow manipulation due to manual operation and (2) severe effects on cell viability due to direct exposure of laser. This dissertation explores the problem of autonomous OT based cell manipulation in the light of addressing the two aforementioned limitations. Microfluidic devices are well suited for the study of biological objects because of their high throughput. Integrating microfluidics with OT provides precise position control as well as high throughput. An automated, physics-aware, planning approach is developed for fast transport of cells in OT assisted microfluidic chambers. The heuristic based planner employs a specific cost function for searching over a novel state-action space representation. The effectiveness of the planning algorithm is demonstrated using both simulation and physical experiments in microfluidic-optical tweezers hybrid manipulation setup. An indirect manipulation approach is developed for preventing cells from high intensity laser. Optically trapped inert microspheres are used for manipulating cells indirectly either by gripping or pushing. A novel planning and control approach is devised to automate the indirect manipulation of cells. The planning algorithm takes the motion constraints of the gripper or pushing formation into account to minimize the manipulation time. Two different types of cells (Saccharomyces cerevisiae and Dictyostelium discoideum) are manipulated to demonstrate the effectiveness of the indirect manipulation approach

Electroencephalography (EEG)-based Brain-Computer Interfaces

International audienceBrain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field