Digital Twin for Industrial Robotics

This thesis aims to develop a Digital Twin model for robot programming that incorporates Virtual Reality (VR). The digital twin is a concept of creating a digital replica of a physical object (such as a robot), which is similar to establishing a model simulation along with some additional functionalities. Simulation software, such as robot operating system (ROS) or other industrial-owned simulation platforms, simulates a robot operation and sends the details to the robot controller. In contrast to this, the Digital Twin model establishes a two-way communication channel between the digital and the physical models. In this thesis, the proposed Digital Twin model can help in online/remote programming of a robotic cell with high accuracy by creating a 3D digital environment of a real-world physical setup, which is similar to watching a movie with 3D glasses. To create a Digital Twin model, the gaming platform is used that comes with specialized plugins for virtual and augmented reality devices. One of the main challenges in any robotic system is writing a code for a defined path and modifying it for future requirements. Programming robot via this traditional approach requires a lot of time and often disturbs the running process. Whereas, in the case of a Digital Twin model, the program can be adjusted or regenerated without disturbing the execution cycle of a physical robot

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version

Augmented Deep Learning Techniques for Robotic State Estimation

While robotic systems may have once been relegated to structured environments and automation style tasks, in recent years these boundaries have begun to erode. As robots begin to operate in largely unstructured environments, it becomes more difficult for them to effectively interpret their surroundings. As sensor technology improves, the amount of data these robots must utilize can quickly become intractable. Additional challenges include environmental noise, dynamic obstacles, and inherent sensor non-linearities. Deep learning techniques have emerged as a way to efficiently deal with these challenges. While end-to-end deep learning can be convenient, challenges such as validation and training requirements can be prohibitive to its use. In order to address these issues, we propose augmenting the power of deep learning techniques with tools such as optimization methods, physics based models, and human expertise. In this work, we present a principled framework for approaching a problem that allows a user to identify the types of augmentation methods and deep learning techniques best suited to their problem. To validate our framework, we consider three different domains: LIDAR based odometry estimation, hybrid soft robotic control, and sonar based underwater mapping. First, we investigate LIDAR based odometry estimation which can be characterized with both high data precision and availability; ideal for augmenting with optimization methods. We propose using denoising autoencoders (DAEs) to address the challenges presented by modern LIDARs. Our proposed approach is comprised of two stages: a novel pre-processing stage for robust feature identification and a scan matching stage for motion estimation. Using real-world data from the University of Michigan North Campus long-term vision and LIDAR dataset (NCLT dataset) as well as the KITTI dataset, we show that our approach generalizes across domains; is capable of reducing the per-estimate error of standard ICP methods on average by 25.5% for the translational component and 57.53% for the rotational component; and is capable of reducing the computation time of state-of-the-art ICP methods by a factor of 7.94 on average while achieving competitive performance. Next, we consider hybrid soft robotic control which has lower data precision due to real-world noise (e.g., friction and manufacturing imperfections). Here, augmenting with model based methods is more appropriate. We present a novel approach for modeling, and classifying between, the system load states introduced when constructing staged soft arm configurations. Our proposed approach is comprised of two stages: an LSTM calibration routine used to identify the current load state and a control input generation step that combines a generalized quasistatic model with the learned load model. We show our method is capable of classifying between different arm configurations at a rate greater than 95%. Additionally, our method is capable of reducing the end-effector error of quasistatic model only control to within 1 cm of our controller baseline. Finally, we examine sonar based underwater mapping. Here, data is so noisy that augmenting with human experts and incorporating some global context is required. We develop a novel framework that enables the real-time 3D reconstruction of underwater environments using features from 2D sonar images. In our approach, a convolutional neural network (CNN) analyzes sonar imagery in real-time and only proposes a small subset of high-quality frames to the human expert for feature annotation. We demonstrate that our approach provides real-time reconstruction capability without loss in classification performance on datasets captured onboard our underwater vehicle while operating in a variety of environments

Multibody dynamics in robotics with focus on contact events

Multibody dynamics methodologies have been fundamental tools utilized to model and simulate robotic systems that experience contact conditions with the surrounding environment, such as in the case of feet and ground interactions. In addressing such problems, it is of paramount importance to accurately and efficiently handle the large body displacement associated with locomotion of robots, as well as the dynamic response related to contact-impact events. Thus, a generic computational approach, based on the Newton-Euler formulation, to represent the gross motion of robotic systems, is revisited in this work. The main kinematic and dynamic features, necessary to obtain the equations of motion, are discussed. A numerical procedure suitable to solve the equations of motion is also presented. The problem of modeling contacts in dynamical systems involves two main tasks, namely the contact detection and the contact resolution, which take into account for the kinematics and dynamics of the contacting bodies, constituting the general framework for the process of modeling and simulating complex contact scenarios. In order to properly model the contact interactions, the contact kinematic properties are established based on the geometry of contacting bodies, which allow to perform the contact detection task. The contact dynamics is represented by continuous contact force models, both in terms of normal and tangential contact directions. Finally, the presented formulations are demonstrated by the application to several robotics systems that involve contact and impact events with surrounding environment. Special emphasis is put on the systems’ dynamic behavior, in terms of performance and stability

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Hopping, Landing, and Balancing with Springs

This work investigates the interaction of a planar double pendulum robot and springs, where the lower body (the leg) has been modified to include a spring-loaded passive prismatic joint. The thesis explores the mechanical advantage of adding a spring to the robot in hopping, landing, and balancing activities by formulating the motion problem as a boundary value problem; and also provides a control strategy for such scenarios. It also analyses the robustness of the developed controller to uncertain spring parameters, and an observer solution is provided to estimate these parameters while the robot is performing a tracking task. Finally, it shows a study of how well IMUs perform in bouncing conditions, which is critical for the proper operation of a hopping robot or a running-legged one