On Model Predictive Path Following and Trajectory Tracking for Industrial Robots

In this article we show how the model predictive path following controller allows robotic manipulators to stop at obstructions in a way that model predictive trajectory tracking controllers cannot. We present both controllers as applied to robotic manipulators, simulations for a two-link manipulator using an interior point solver, consider discretization of the optimal control problem using collocation or Runge-Kutta, and discuss the real-time viability of our implementation of the model predictive path following controller.Comment: Draft of article for CASE 201

Constraint Definition for Gripper Selection and Grasp Planning for Robotic Assembly Using Product Manufacturing Information from STEP AP242Ed2 Files

This article uses the Product Manufacturing Information (PMI) from STEP AP242 neutral files for gripper selection and grasp planning in a robotic assembly operation. The PMI, along with the part geometry and dimensions, are used in identifying various handling features of the parts and selecting an appropriate gripper. The required PMI, like material, volume, surface finish, threading and coating information, are added to the STEP AP242 files. The PMI is semantically included in the STEP files following the Model Based Definition (MBD) methodology. Two methods are described to add the PMI to the STEP files, one using a custom string and another using the standard entities defined in ISO 10303 AP242: 2020 standard. The entire process is demonstrated in a use case.publishedVersio

On Robotic Assembly and Optimization-Based Control of Industrial Manipulators

Robotic manipulators are essential tools of industry. They are ubiquituous in modern factories, and they are starting to become more commonplace everywhere else. The trend in robotic manipulators is to move towards allowing for more human-robot collaboration, to allow for sensor-based tasks, and to perform classical industrial tasks such as assembly in new and unforeseen environments. These trends enable small and medium businesses to lower the cost of automation by simplifying robot programming. The core interest of this thesis is an idealized automatic assembly system, capable of generating and executing robot programs based on CAD models. Such a system is challenging to realize, as it is composed of many subcomponents that are large research topics in and of themselves. From the control-theoretic perspective, much of the work lies in task specification and relating assembly of the parts to robot motions, as is the core discussion in one of the papers. The papers present control strategies intended for on-line control of an industrial manipulator. As optimization techniques may have slow execution time, the timings of the different control strategies are a recurring topic in the papers. The communication and tracking latency of a KUKA robot system is also investigated when developing an open-source control interface to the KUKA system. This thesis presents contributions in robotic assembly and optimization based control of industrial manipulators. The papers in this thesis aim to address some shortcomings in the robotics literature that were found when investigating the idealized system. The accompanying text gives a brief introduction to the history of robotics, as well as an overview of some of the robotics literature

Sensor Fusion with Out-of-Sequence Measurements - Localization in an Agricultural Robot Using Visual Odometry

Timing is an important aspect when working with visual odometry (VO). The time of processing and data-transfer of image-based measurements are significantly longer than IMU, wheel encoders, and GPS measurements. This introduces an arrival latency, the VO measurements require delay fusion, a subcategory of out-of-sequence measurement (OOSM) fusion. In cooperation with Adigo AS this thesis has focused on the Asterix project, where an agricultural robot uses a downward facing camera for visual-inertial odometry to aid in localization. The main focus in this work is with the delay fusion problem. By approaching the OOSM fusion with a Bayesian framework, the theory chapter presents a generalization of stochastic cloning. A byproduct of the investigation is an unscented multiple-point smoother capable of defining fixed-points to be smoothed on demand. Simulations and experiments showed that the OOSM fusion methods worked, but model inconsistencies and inaccuracies in the VO measurements negatively affected the results

Stability of the Tracking Problem with Task-Priority Inverse Kinematics

The multiple task-priority inverse kinematics framework using the Moore-Penrose pseudoinverse has been shown to be asymptotically stable for the regulation problem with certain conditions on the tasks. In this article we present a theorem that extends this to the tracking problem by including an additional criterion that we term fully represented in the null-space. We show the effect of this on a simulation with a snake-like robot manipulator with 30 links for 3 compatible tasks, and an example of 2 tasks that are compatible as a regulation problem but incompatible as a tracking problem. As the tracking problem is more affected by the linearization assumption, we also include an example showing that the effect of linearization can be detrimental during tracking

On the globally exponentially convergent immersion and invariance speed observer for mechanical systems

In this article we present a reformulation of the invariance and immersion speed observer of Astolfi et al. as applied to mechanical systems with bounded inertia matrices. This is done to explore the possibility of its practical implementation e.g. for 6 degrees-of-freedom industrial robots. The reformulation allows us find an explicit expression for one of the bounds used in the observer, and a constructive method for the second. We show that the observer requires either analytically or numerically solving at most 2n2 integrals, where n is the number of generalized coordinates in the mechanical system.acceptedVersio

Constraint Definition for Gripper Selection and Grasp Planning for Robotic Assembly Using Product Manufacturing Information from STEP AP242Ed2 Files

This article uses the Product Manufacturing Information (PMI) from STEP AP242 neutral files for gripper selection and grasp planning in a robotic assembly operation. The PMI, along with the part geometry and dimensions, are used in identifying various handling features of the parts and selecting an appropriate gripper. The required PMI, like material, volume, surface finish, threading and coating information, are added to the STEP AP242 files. The PMI is semantically included in the STEP files following the Model Based Definition (MBD) methodology. Two methods are described to add the PMI to the STEP files, one using a custom string and another using the standard entities defined in ISO 10303 AP242: 2020 standard. The entire process is demonstrated in a use case

Leveraging Model Based Definition and STEP AP242 in Task Specification for Robotic Assembly

This article explores using aspects of STEP AP242 for constraint-based robot programming for assembly operations. Industry 4.0 envisions smart, connected factories where all the operations are connected by an unbroken thread of product and process data. As part of these efforts, many industries are adopting Model Based Definition and STEP AP242. STEP AP242 is an exchange format that allows Model Based Definition where Product Manufacturing Information is directly associated with the 3D CAD model. This article relates the geometric and assembly constraints from the CAD model to motion constraints on the robot during the assembly process. This article also discusses the use of Product Manufacturing Information from STEP AP242 files for automatic robot programming. The results are showcased with a prototype for a motor assembly scenario