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A Robotic Platform for Building and Exploiting Digital Product Memories
Abstract and Figures
This chapter presents the design of the robotic platform AILA, a mobile dual-arm robot system developed as a research platform for investigating aspects of the currently booming multidisciplinary area of mobile manipulation. The robot integrates and allows in a single platform performance of research in most of the areas involved in autonomous robotics: navigation, mobile and dual-arm manipulation planning, active compliance and force control strategies, object recognition, scene representation, and semantic perception. AILA has 32 degrees of freedom (DOF), including 7-DOF arms, a 4-DOF torso, a 2-DOF head, and a mobile base equipped with 6 wheels, each of them with 2 degrees of freedom. Additionally, the left hand of the robot was equipped with a RFID reader in order to receive the information coming from the DPM. This chapter provides an overview of the design, the variety of sensors incorporated in the system, and its required control software.
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... holding objects. AILA [23] is a humanoid, mobile dual-arm robot, which was originally developed to investigate aspects of mobile manipulation. A robot like AILA is an ideal candidate for a real world communication partner for humans. ...
... holding objects. AILA [22] is a humanoid, mobile dual- arm robot, which was originally developed to investigate aspects of mobile ma- nipulation. A robot like AILA is an ideal candidate for a real world communica- tion partner for humans. ...
With the increasing capabilities of agents using Artificial Intelligence, an opportunity opens up to form team like collaboration between humans and artificial agents. This paper describes the setting-up of a Hybrid Team consisting of humans, robots, virtual characters and softbots. The team is situated in a flexible industrial production. The work presented here focuses on the central architecture and the characteristics of the team members and components. To achieve the overall team goals, several challenges have to be met to find a balance between autonomous behaviors of individual agents and coordinated teamwork.
Low-cost and compact digital storage, sensors and radio modules make it possible to embed a digital memory into a product for recording all relevant events throughout the entire lifecycle of the artifact. By capturing and interpreting ambient conditions and user actions, such computationally enhanced products have a data shadow and are able to perceive and control their environment, to analyze their observations and to communicate with other smart objects and human users about their lifelog data. In the introductory section of this chapter, we illustrate the innovation and application potential offered through the concept of semantic product memories by an imaginative scenario. Then we provide a taxonomy of the wide variety of digital object memories: from mobile cyber-physical systems to semantic product memories in open-loop applications. We show that extended customer information, traceability and increased quality assurance have been the drivers for the rudimentary forerunners of product memories in the food industry. Then we discuss the benefits and risks of semantic product memories for producers as well as consumers. We argue that active semantic product memories will play a key role in the upcoming fourth industrial revolution based on cyber-physical production systems. Finally, we provide an overview of the structure and content of the remainder of this book.
In the SemProM project “Products Keep a Diary” smart labels give products a memory and support intelligent logistics. Within the ICT 2020 research program of the German Federal Ministry of Education and Research the Innovation Alliance “Digital Product Memory” (IA DPM) is developing key technologies for the Internet of Things in the cooperative project SemProM. Through the use of integrated sensors, relations in the production process become transparent and supply chains as well as environmental influences traceable. The producer is supported and the consumer better informed about the product.
On its way along the supply chain, a product may be exposed to physical actors with very different requirements for the interaction with a DPM. For instance, while human users may precisely perceive a given product’s visual shape, they have to rely on a “mediating device” in order to create and apply content stored in a DPM. In contrast, robots may directly access the data stored in a DPM, but may require specific data in order to get a better “understanding” of a physical interaction task. Finally, DPMs may have to interact with other DPMs in their surroundings, for instance, in order to delegate communication tasks. This chapter reviews components of the access layer, a part of the SEMPROM interaction architecture which has been introduced to support tasks particularly common to the interaction of humans, robots, and DPMs with DPMs.
Industrial production and supply chains face increased demands for mass customization and tightening regulations on the traceability of goods, leading to higher requirements concerning flexibility, adaptability, and transparency of processes. Technologies for the “Internet of Things” such as smart products and semantic representations pave the way for future factories and supply chains to fulfill these challenging market demands. In this chapter a backend-independent approach for information exchange in open-loop production processes based on Digital Product Memories (DPMs) is presented. By storing order-related data directly on the item, relevant lifecycle information is attached to the product itself. In this way, information handover between several stages of the value chain with focus on the manufacturing phase of a product has been realized. In order to report best practices regarding the application of DPM in the domain of industrial production, system prototype implementations focusing on the use case of producing and handling a smart drug case are illustrated.
This paper presents a humanoid two-arm system developed as a research platform for studying dexterous two-handed manipulation. The system is based on the modular DLR-Lightweight-Robot-III and the DLR-Hand-II. Two arms and hands are combined with a three degrees-of-freedom movable torso and a visual system to form a complete humanoid upper body. In this paper we present the design considerations and give an overview of the different sub-systems. Then, we describe the requirements on the software architecture. Moreover, the applied control methods for two-armed manipulation and the vision algorithms used for scene analysis are discussed.
A simple and efficient randomized algorithm is presented for
solving single-query path planning problems in high-dimensional
configuration spaces. The method works by incrementally building two
rapidly-exploring random trees (RRTs) rooted at the start and the goal
configurations. The trees each explore space around them and also
advance towards each other through, the use of a simple greedy
heuristic. Although originally designed to plan motions for a human arm
(modeled as a 7-DOF kinematic chain) for the automatic graphic animation
of collision-free grasping and manipulation tasks, the algorithm has
been successfully applied to a variety of path planning problems.
Computed examples include generating collision-free motions for rigid
objects in 2D and 3D, and collision-free manipulation motions for a
6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is
also presented
In this paper, a new joint actuator is introduced which builds the basis for the newly developed SpaceClimber robot by the German Research Center for Artificial Intelligence. Based on in-house developed joint actuators for ambulating robots, this complete new design combines performance, stability, and space-related components. The newly developed on-board electronics enables the possibility of a biologically inspired functionality like decentralized autonomous joint control. In this paper, we explain the design and the control architecture of the actuator. We describe the selected components and present the fully functional prototype. The results of the first performance experiments are presented.
Abstract One of the challenges in developing real-world autonomous,robots is the need for integrating and rigorously test- ing high-level scripting, motion planning, perception, and control algorithms. For this purpose, we introduce an open-source cross-platform software architecture called OpenRAVE, the Open Robotics and Animation Virtual Envi- ronment. OpenRAVE is targeted for real-world autonomous robot applications, and includes a seamless integration of 3-D simulation, visualization, planning, scripting and control. A plugin architecture allows users to easily write cus- tom controllers or extend functionality. With OpenRAVE plugins, any planning algorithm, robot controller, or sensing subsystem can be distributed and dynamically loaded at run-time, which frees developers from struggling with mono- lithic code-bases. Users of OpenRAVE can concentrate on the development,of planning and scripting aspects of a problem without having to explicitly manage the details of robot kinematics and dynamics, collision detection, world updates, and robot control. The OpenRAVE architecture provides a flexible interface that can be used in conjunction with other popular robotics packages such as Player and ROS because it is focused on autonomous,motion planning and high-level scripting rather than low-level control and message,protocols. OpenRAVE also supports a powerful network scripting environment,which makes,it simple to control and monitor robots and change execution flow dur- ing run-time. One of the key advantages of open component,architectures is that they enable the robotics research community,to easily share and compare,algorithms. II Contents
Abstract— This paper gives an overview of ROS, an open- source robot operating,system. ROS is not an operating,system in the traditional sense of process management,and scheduling; rather, it provides a structured communications layer above the host operating,systems,of a heterogenous,compute,cluster. In this paper, we discuss how ROS relates to existing robot software frameworks, and briefly overview some of the available application software,which,uses ROS.
The classical filtering and prediction problem is re-examined using the Bode-Sliannon representation of random processes and the “state-transition” method of analysis of dynamic systems. New results are: (1) The formulation and methods of solution of the problem apply without modification to stationary and nonstationary statistics and to growing-memory and infinitememory filters. (2) A nonlinear difference (or differential) equation is derived for the covariance matrix of the optimal estimation error. From the solution of this equation the coefficients of the difference (or differential) equation of the optimal linear filter are obtained without further calculations. (3) The filtering problem is shown to be the dual of the noise-free regulator problem. The new method developed here is applied to two well-known problems, confirming and extending earlier results. The discussion is largely self-contained and proceeds from first principles; basic concepts of the theory of random processes are reviewed in the Appendix.
A Python application programming interface (API) called SMACH, based on hierarchical concurrent state machines, allow executives to be controlled by a higher level task-planning system. SMACH is a ROS independent library that can be used to build hierarchical and concurrent state machines and any other task-state container that adheres to the provided interfaces. SMACH States represent states of execution, each with some set of potential outcomes, and implement a blocking execute function, which runs until it returns a given outcome. A simple execution policy the SMACH Concurrence executes one state at a time in series and executes more than one state simultaneously. Each SMACH container has a locally scoped dictionary of user data that can be accessed by each of its child states, allowing states to access data that was written by previously executed states.
A third generation of torque-controlled light weight robots has been developed in DLR's robotics and mechatronics lab which is based on all the experiences that have been had with the first two generations. It aims at reaching the limits of what seems achievable with present day technologies not only with respect to light-weight, but also with respect to minimal power consumption and losses. One of the main gaps we tried to close in version III was the development of a new, robot-dedicated high energy motor designed with the best available techniques of concurrent engineering, and the renewed efforts to save weight in the links by using ultralight carbon fibres.