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It is difficult for robots to handle a vibrating deformable object. Even for human beings it is a high-risk operation to, for example, insert a vibrating linear object into a small hole. However, fast manipulation using a robot arm is not just a dream; it may be achieved if some important features of the vibration are detected online. In this paper, we present an approach for fast manipulation using a force/torque sensor mounted on the robot's wrist. Template matching method is employed to recognize the vibrational phase of the deformable objects. Therefore, a fast manipulation can be performed with a high success rate, even if there is acute vibration. Experiments inserting a deformable object into a hole are conducted to test the presented method. Results demonstrate that the presented sensor-based online fast manipulation is feasible.
This paper discusses the problem of automatic off-line programming and motion planning for industrial robots. At first, a new concept consisting of three steps is proposed. The first step, a new method for on-line motion planning is introduced. The motion planning method is based on the A*-search algorithm and works in the implicit configuration space. During searching, the collisions are detected in the explicitly represented Cartesian workspace by hierarchical distance computation. In the second step, the trajectory planner has to transform the path into a time and energy optimal robot program. The practical application of these two steps strongly depends on the method for robot calibration with high accuracy, thus, mapping the virtual world onto the real world, which is discussed in the third step.
This paper presents a new approach to parallel motion planning for industrial robot arms with six degrees of freedom in an on-line given 3D environment. The method is based on the A-search algorithm and needs no essential off-line computations. The algorithm works in an implicitly descrete configuration space. Collisions are detected in the Cartesian workspace by hierarchical distance computation based on the given CAD model. By decomposing the 6D configuration space into hypercubes and cyclically mapping them onto multiple processing units, a good load distribution can be achieved. We have implemented the parallel motion planner on a workstation cluster with 9 PCs and tested the planner for several benchmark environments. With optimal discretisation, the new approach usually shows linear speedups. In on-line provided environments with static obstacles, the parallel planning times are only a few seconds.
A practical distributed planning and control system for industrial robots is presented. The hierarchical concept consists of three independent levels. Each level is modularly implemented and supplies an application interface (API) to the next higher level. At the top level, we propose an automatic motion planner. The motion planner is based on a best-first search algorithm and needs no essential off-line computations. At the middle level, we propose a PC-based robot control architecture, which can easily be adapted to any industrial kinematics and application. Based on a client/server-principle, the control unit estab-lishes an open user interface for including application specific programs. At the bottom level, we propose a flexible and modular concept for the integration of the distributed motion control units based on the CAN bus. The concept allows an on-line adaptation of the control parameters according to the robot's configuration. This implies high accuracy for the path execution and improves the overall system performance.
This paper presents a new approach to parallel motion planning for industrial robot arms with six degrees of freedom in an on-line given 3D environment. The method is based on the A*-search algorithm and needs no essential off-line computations. The algorithm works in an implicitly descrete configuration space. Collisions are detected in the cartesian workspace by hierarchical distance computation based on the given CAD model. By decomposing the 6D configuration space into hypercubes and cyclically mapping them onto multiple processing units, a good load distribution can be achieved. We have implemented the parallel motion planner on a workstation cluster with 9 PCs and tested the planner for several benchmark environments. With optimal discretisation, the new approach usually shows linear, and sometimes even superlinear speedups. In on-line provided environments with static obstacles, the parallel planning times are only a few seconds.
A new problem for the automated off-line programming of industrial robot application is investigated. The Multi-Goal Path Planning is to find the collision-free path connecting a set of goal poses and minimizing e.g. the total path length. Our solution is based on an earlier reported path planner for industrial robot arms with 6 degrees-of-freedom in an on-line given 3D environment. To control the path planner, four different goal selection methods are introduced and compared. While the Random and the Nearest Pair Selection methods can be used with any path planner, the Nearest Goal and the Adaptive Pair Selection method are favorable for our planner. With the latter two goal selection methods, the Multi-Goal Path Planning task can be significantly accelerated, because they are able to automatically solve the simplest path planning problems first. Summarizing, compared to Random or Nearest Pair Selection, this new Multi-Goal Path Planning approach results in a further cost reduction of the programming phase.
INRECA offers tools and methods for developing, validating, and maintaining classification, diagnosis and decision support systems. INRECA's basic technologies are inductive and case-based reasoning [9]. INRECA fully integrates [2] both techniques within one environment and uses the respective advantages of both technologies. Its object-oriented representation language CASUEL [10, 3] allows the definition of complex case structures, relations, similarity measures, as well as background knowledge to be used for adaptation. The objectoriented representation language makes INRECA a domain independent tool for its destined kind of tasks. When problems are solved via case-based reasoning, the primary kind of knowledge that is used during problem solving is the very specific knowledge contained in the cases. However, in many situations this specific knowledge by itself is not sufficient or appropriate to cope with all requirements of an application. Very often, background knowledge is available and/or necessary to better explore and interpret the available cases [1]. Such general knowledge may state dependencies between certain case features and can be used to infer additional, previously unknown features from the known ones.
Edit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small-scale perturbations. For trees with 𝒪(n) nodes, we describe an 𝒪(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition-independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real-world examples to demonstrate its practicality and utility.
Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only kinematic data. We propose the biomechanically inspired force shadow method (FSM) to obtain a unique solution for any contact phase, including double support, of an arbitrary motion. We create a kinematic based function, model an anatomical foot shape and mimic the effect of hip muscle activations. We compare our estimations with the measurements of a Zebris pressure plate and obtain correlations of 0.39≤r≤0.94 for double support motions and 0.83≤r≤0.87 for a walking motion. The presented data is based on inertial human motion capture, showing the applicability for scenarios outside the laboratory. The proposed approach has low computational complexity and allows for online vGRF estimation.
About the approach The approach of TOPO was originally developed in the FABEL project1[1] to support architects in designing buildings with complex installations. Supplementing knowledge-based design tools, which are available only for selected subtasks, TOPO aims to cover the whole design process. To that aim, it relies almost exclusively on archived plans. Input to TOPO is a partial plan, and output is an elaborated plan. The input plan constitutes the query case and the archived plans form the case base with the source cases. A plan is a set of design objects. Each design object is defined by some semantic attributes and by its bounding box in a 3-dimensional coordinate system. TOPO supports the elaboration of plans by adding design objects.
Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the 3D gait kinematics, spatio temporal parameters (Set 1) and joint angles (Set 2), of an inertial sensor (IMU) system are proposed as an input for a support vector machine (SVM) model, to differentiate impaired and non-impaired gait. The features were divided into two subsets. The IMU-based features were validated against an optical motion capture (OMC) system by means of 20 patients after THA and a healthy control group of 24 subjects. Then the SVM model was trained on both subsets. The validation of the IMU system-based kinematic features revealed root mean squared errors in the joint kinematics from 0.24° to 1.25°. The validity of the spatio-temporal gait parameters (STP) revealed a similarly high accuracy. The SVM models based on IMU data showed an accuracy of 87.2% (Set 1) and 97.0% (Set 2). The current work presents valid IMU-based features, employed in an SVM model for the classification of the gait of patients after THA and a healthy control. The study reveals that the features of Set 2 are more significant concerning the classification problem. The present IMU system proves its potential to provide accurate features for the incorporation in a mobile gait-feedback system for patients after THA.
3D joint kinematics can provide important information about the quality of movements. Optical motion capture systems (OMC) are considered the gold standard in motion analysis. However, in recent years, inertial measurement units (IMU) have become a promising alternative. The aim of this study was to validate IMU-based 3D joint kinematics of the lower extremities during different movements. Twenty-eight healthy subjects participated in this study. They performed bilateral squats (SQ), single-leg squats (SLS) and countermovement jumps (CMJ). The IMU kinematics was calculated using a recently-described sensor-fusion algorithm. A marker based OMC system served as a reference. Only the technical error based on algorithm performance was considered, incorporating OMC data for the calibration, initialization, and a biomechanical model. To evaluate the validity of IMU-based 3D joint kinematics, root mean squared error (RMSE), range of motion error (ROME), Bland-Altman (BA) analysis as well as the coefficient of multiple correlation (CMC) were calculated. The evaluation was twofold. First, the IMU data was compared to OMC data based on marker clusters; and, second based on skin markers attached to anatomical landmarks. The first evaluation revealed means for RMSE and ROME for all joints and tasks below 3°. The more dynamic task, CMJ, revealed error measures approximately 1° higher than the remaining tasks. Mean CMC values ranged from 0.77 to 1 over all joint angles and all tasks. The second evaluation showed an increase in the RMSE of 2.28°– 2.58° on average for all joints and tasks. Hip flexion revealed the highest average RMSE in all tasks (4.87°– 8.27°). The present study revealed a valid IMU-based approach for the measurement of 3D joint kinematics in functional movements of varying demands. The high validity of the results encourages further development and the extension of the present approach into clinical settings.
In many robotic applications, the teaching of points in space is necessary to register the robot coordinate system with the one of the application. Robot-human interaction is awkward and dangerous for the human because of the possibly large size and power of the robot, so robot movements must be predictable and natural. We present a novel hybrid control algorithm which provides the needed precision in small scale movements while allowing for fast and intuitive large scale translations.
Machine learning algorithms are widely applied to create powerful prediction models. With increasingly complex models, humans' ability to understand the decision function (that maps from a high-dimensional input space) is quickly exceeded. To explain a model's decisions, black-box methods have been proposed that provide either non-linear maps of the global topology of the decision boundary, or samples that allow approximating it locally. The former loses information about distances in input space, while the latter only provides statements about given samples, but lacks a focus on the underlying model for precise ‘What-If'-reasoning. In this paper, we integrate both approaches and propose an interactive exploration method using local linear maps of the decision space. We create the maps on high-dimensional hyperplanes—2D-slices of the high-dimensional parameter space—based on statistical and personal feature mutability and guided by feature importance. We complement the proposed workflow with established model inspection techniques to provide orientation and guidance. We demonstrate our approach on real-world datasets and illustrate that it allows identification of instance-based decision boundary structures and can answer multi-dimensional ‘What-If'-questions, thereby identifying counterfactual scenarios visually.
In order to reduce the elapsed time of a computation, a pop-ular approach is to decompose the program into a collection of largelyindependent subtasks which are executed in parallel. Unfortunately, it isoften observed that tightly-coupled parallel programs run considerablyslower than initially expected. In this paper, a framework for the anal-ysis of parallel programs and their potential speedup is presented. Twoparameters which strongly affect the scalability of parallelism are iden-tified, namely the grain of synchronization, and the degree to which thetarget hardware is available. It is shown that for certain classes of appli-cations speedup is inherently poor, even if the program runs under theidealized conditions of perfect load balance, unbounded communicationbandwidth and negligible communication and parallelization overhead.Upper bounds are derived for the speedup that can be obtained in threedifferent types of computations. An example illustrates the main find-ings.
This paper deals with the handling of deformable linear objects (DLOs), such as hoses, wires or leaf springs. It investigates the a priori knowledge about the 6-dimensional force/torque signal for a changing contact situation between a DLO and a rigid polyhedral obstacle. The result is a complete list, containing for each contact change the most significant combination of force/torque signal components together with a description of the expected signal curve. This knowledge enables the reliable detection of changes in the DLO contact situation and with it the implementation of sensor-based manipulation skills for all possible contact changes.
The development of algorithmic differentiation (AD) tools focuses mostly on handling floating point types in the target language. Taping optimizations in these tools mostly focus on specific operations like matrix vector products. Aggregated types like std::complex are usually handled by specifying the AD type as a template argument. This approach provides exact results, but prevents the use of expression templates. If AD tools are extended and specialized such that aggregated types can be added to the expression framework, then this will result in reduced memory utilization and improve the timing for applications where aggregated types such as complex number or matrix vector operations are used. Such an integration requires a reformulation of the stored data per expression and a rework of the tape evaluation process. We will demonstrate the overheads on a synthetic benchmark and show the improvement when aggregated types are handled properly by the expression framework of the AD tool.
Several activities around the world aim at integrating object-oriented data models with relational ones in order to improve database management systems. As a first result of these activities, object-relational database management systems (ORDBMS) are already commercially available and, simultaneously, are subject to several research projects. This (position) paper reports on our activities in exploiting object-relational database technology for establishing repository manager functionality supporting software engineering (SE) processes. We argue that some of the key features of ORDBMS can directly be exploited to fulfill many of the needs of SE processes. Thus, ORDBMS, as we think, are much better suited to support SE applications than any others. Nevertheless, additional functionality, e. g., providing adequate version management, is required in order to gain a completely satisfying SE repository. In order to remain flexible, we have developed a generative approach for providing this additional functionality. It remains to be seen whether this approach, in turn, can effectively exploit ORDBMS features. This paper, therefore, wants to show that ORDBMS can substantially contribute to both establishing and running SE repositories.
This paper deals with the robust manipulation of deformable linear objects such as hoses or wires. We propose manipulation based on thequalitative contact state between the deformable workpiece and a rigid environment. First, we give an enumeration of possible contact states and discuss the main characteristics of each state. Second, we investigate the transitions which are possible between the contact states and derive criteria and conditions for each of them. Finally, we apply the concept of contact states and state transitions to the description of a typical assembly task.