The latency in vision-based sensor systems used in human-robot collaborative environments is an important safety parameter which in most cases has been neglected by researchers. The main reason for this neglect is the lack of an accurate ground-truth sensor system with a minimal delay to benchmark the vision-sensors against. In this paper the latencies of 3D vision-based sensors are experimentally evaluated and analyzed using an accurate laser-tracker system which communicates on a dedicated EtherCAT channel with minimal delay. The experimental results in the paper demonstrate that the latency in the vision-based sensor system is many orders higher than the latency in the control and actuation system.

Relative Vessel-to-Vessel Motion Compensation (VVMC) between two floating vessels can be considered an extension of existing Active Heave Compensation (AHC), which is current state-of-the-art in the offshore industry. In this paper, the experimental results from a small-scale experimental setup of a VVMC scenario are presented. The work focuses on the kinematics related to VVMC, sensor accuracy of Motion Reference Units (MRUs), and the communication delay related to wireless transmission of MRU data between two floating vessels. The accuracies of the MRU sensors are good and verified to be less than 5% of the heave amplitude. The wireless communication link had a typical time-delay of 8ms. The presented work demonstrates the 3D-kinematics related to VVMC using homogeneous transformations, and the experimental results show that wireless transmission of MRU data is a viable solution for offshore VVMC scenarios.

The proposed paper presents a method to optimally select components of a drivetrain for an electrically actuated machine. A simple mathematical model of the machine is established and inequality constraints which determine the choice of drivetrain components are formulated. Elements to be picked (namely, a motor, a gearbox, and a drive) are taken from a discrete set of data provided in the catalogs of industrial motors and drives manufacturers. By solving an optimization problem, a combination of components which both satisfy design requirements and minimize the total drivetrain cost is selected. The operation of the selected drivetrain is verified against the motor loadability curves. In addition, feasibility of other possible drivetrain configurations is checked and benchmarked with the optimal solution. Practical significance of the current work is demonstrated on a winch mechanism which is a popular part of many engineering applications, however, methods presented here could easily be adapted to other machines and industries. The results of the current work allow to reduce conservatism when designing actuation systems, while still satisfying the safety requirements specified by the designer. The system operating conditions are therefore effectively shifted to be closer to the constraints, which results in increasing the overall efficiency of the design and proving its cost-effectiveness.

In this paper the developed method for off-line compensation of tool deflections when milling aluminum with an industrial robot is presented. The efficiency of this approach is verified with high precision measurements of deflections using a laser tracker. The compensation method includes both the static milling process model which can predict the mean value components of the tool forces and a new combined local/global approach for estimating the combined stiffnesses of joints. With a process model such as the one presented in this paper and estimates of the robot's joint stiffness values, the tool path can be adjusted to counteract deflections of the tool during milling operations. The model was estimated from a large set of machining experiments and is valid for a given tool and material. The stiffness of each joint is a combination of several effects: (i) stiffness of the links, (ii) stiffness of joint bearings and gears and (iii) stiffness of the position control loops given by the individual axis controller gains in the controller software. The method presented in this paper increases accuracy when using an industrial robot for milling in relatively hard materials.

In this paper we present an experimental study on real-time collision avoidance with potential fields that are based on 3D point cloud data and processed on the Graphics Processing Unit (GPU). The virtual forces from the potential fields serve two purposes. First, they are used for changing the reference trajectory. Second they are projected to and applied on torque control level for generating according nullspace behavior together with a Cartesian impedance main control loop. The GPU algorithm creates a map representation that is quickly accessible. In addition, outliers and the robot structure are efficiently removed from the data, and the resolution of the representation can be easily adjusted. Based on the 3D robot representation and the remaining 3D environment data, the virtual forces that are fed to the trajectory planning and torque controller are calculated. The algorithm is experimentally verified with a 7-Degree of Freedom (DoF) torque controlled KUKA/DLR Lightweight Robot for static and dynamic environmental conditions. To the authors knowledge, this is the first time that collision avoidance is demonstrated in real-time on a real robot using parallel GPU processing.

In this paper a new step-wise approach to kinematic calibration of a 5-DOF Gantry-Tau parallel kinematic machine (PKM) is presented. The approach can be adapted to the modular design of the PKM and the calibration could easily perform part of the assembly instructions for the machine. By using measurements from a laser tracker and least-squares estimates of polynomial functions, a typical accuracy of about 20 micrometer was achieved for the base actuators. The remaining set of 30 general parameters for the hexapod link structure and spherical joint connections were successfully estimated using the Complex search-based evolutionary algorithm.

In this paper the invited session and the 4 original contributions are described first. Then the details of the first paper in the invited session is presented. In this first paper a new method for calculating the dynamic stiffness (frequency response) of parallel kinematic machines (PKMs) with six links, or Hexapods, is presented. The method assumes that each link and universal joint can be described by a mass-spring-damper model and calculates the transfer function from a Cartesian force or torque to Cartesian position or orientation. The new method has been used to benchmark two versions of the Gantry-Tau PKM, a relatively new PKM which has been designed to achieve a large workspace to installation space ratio, while at the same time achieve higher static and dynamic stiffness compared to serial-type industrial robots. The benchmark results presented in this paper show that these goals can be achieved in large sections of the workspace envelope. The benchmark also shows that machining centre specifications can be met if the location and dimensions of the work objects are known at the design stage.

A new identification procedure for a linear actuator used in parallel kinematic manipulators has been developed. The actuator dynamics contain both hysteresis and backlash resulting in a highly nonlinear system. The results in this paper show that not only can a nonlinear model of the system be successfully identified from measurement data, but the model is also compact enough to be an ideal candidate for inclusion in a high-performance robot control system

This paper presents a new error kinematic model containing twelve parameters for the three linear actuators of the Gantry-Tau parallel kinematic manipulator. Three different types of measurement equipment, laser interferometers, linear encoders and double-ball bars, can successfully be used to calibrate the linear actuators. A method for selecting an optimal set of Cartesian coordinates is presented and a small set of fifty coordinates have been found to be sufficient. The methods presented in this paper allow for a fast and easy set up calibration procedure of the Gantry-Tau after assembly and installation, which is a pre-requisite for flexible automation with parallel kinematic manipulators

This paper describes the workspace of the 5-axis variant of the Gantry-Tau parallel manipulator. The 5-axis movements are achieved by extending the 3-axis Gantry-Tau with two linear telescope link actuators. A new analytic inverse kinematic solution for the 5-axis machine and an analysis of the collision-free zones of the machine are presented. The results show that by careful design of the manipulated platform, a collision-free 5-axis workspace can be achieved. The achievable re-orientation of 30 degrees in the entire workspace is large for a parallel kinematic machine

The problem of controlling sensory input and perception for use in mobile navigation is addressed in this paper. The proposed solution offers several advantages compared to existing methods in the literature. First, the proposed solution is based on a stochastic dynamic programming algorithm which guarantees cost-efficiency of the real-time sensory perception controller (SPC) by solving a constrained optimisation problem. Second, a new and unique discrete event model of mobile navigation is presented. The model is task-independent and can be used in a wide range of navigation problems. Third, the discrete events become a natural common representational format for the sensors, which further extends the applicability of the proposed solution. Fourth, the sensing aspect of discrete event systems has often been neglected in the literature. In this paper we present a unique approach to online discrete event identification.

In this paper we address the problem of controlling sensory perception for use in discrete event feedback control systems. The process of controlling sensory perception has two main objectives: 1) to collect perceptual information to identify events with high levels of confidence and 2) to keep the sensing costs low. Several event recognition techniques have been developed by the authors, where each of the event recognisers produces confidence levels of recognised events. For a discrete event control system running in normal operation, the confidence levels are typically large and only a few event recognisers are needed. Then, as the event recognition becomes harder, the confidence levels will decrease and additional event recognisers are utilised by a sensory perception controller (SPC). Hence, the overall system has the ability to actively control the use of sensory input and perception to achieve high performance discrete event recognition. Two applications are presented in the paper: 1) robotic assembly and 2) mobile navigation. Both applications fit well in the discrete event framework and demonstrate the benefits of sensory perception control.

A new technique for selecting, in real time, different sensing techniques for a robotic system has been developed. The proposed method is based on stochastic dynamic programming, which provides an effective solution to multi-stage decision problems. At each stage in the decision process a sensor selection controller has the option of consulting a new process monitoring technique to improve the knowledge of the task or terminating the decision process without any further information gathering. The sensor selection controller has been successfully implemented for the real-time control of a planar robotic assembly task in a discrete event control framework. One of the monitoring methods used is based on hidden Markov models, where the average recognition rate was 87%. The rate of 87% was chosen to show the effectiveness of the dynamic sensor selection method. The experiments show that the method performs better than any individual process monitor. A successful event recognition rate of 97% with an average CPU time of 0.38 seconds is achieved when two force monitors and one position monitor are available to the sensor selection controller.

A method for combining dynamic force and static position measurements for the monitoring of assembly is presented. A multilayer perceptron (MLP) network is used as a classifier where the individual network outputs correspond to contact state transitions occuring during the assembly process. When a contact state transition occurs, the MLP output with the largest value is chosen. The recognised contact state is sent to a discrete event controller which guides the workpiece through a series of contact states to the final desired configuration. The MLP has been successfully implemented on a Motorola 68040 based VxWorks board with successful recognition rates of 94.4% and 92.0% on a training set and an independent test set, respectively.

Discrete event recognition based on force measurements in the frequency-domain, is presented. The force signals arise from interaction between the workpiece and the environment in a planar assembly task. The discrete events are modeled as hidden Markov models (HMMs), where the models are trained off-line with the Baum-Welch re-estimation algorithm. After the HMMs have been trained, we use them online in a robotic system to recognise discrete events as they occur. Event recognition with an accuracy as high as 98% was accomplished in 0.5-0.6 s with a relatively small training set.

A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures.