Robot Programming Language Research Articles

Sensorless robotized cable contour following and connector detection

This paper focuses on the identification of the connector of a semi-deformable object (SDLO) (i.e. a deformable element as a cable featuring rigid parts as connectors) to perform insertion and disconnection tasks. We propose a cable tracing skill that allows a robotic arm to follow the unknown contour of a cable held for a small portion by a fixture, and to identify its connector that can be grasped and inserted or disconnected. No prior knowledge of the SDLO’s length and shape is required. To slide along the cable, the last joint of the manipulator is made compliant to adapt to the SDLO shape and a recursive least squares algorithm with forgetting factor is exploited to estimate the future local shape of the cable and to speed up the contour following procedure. The detection of the connector is based on the estimated contact force acting on the robot’s end-effector while sliding along the SDLO. Remarkably, the method does not require vision, tactile and/or force sensors, it makes use just of a parallel gripper equipped with simple fingertips, and it can be implemented directly in a generic robot programming language. The effectiveness of our strategy is assessed via experimental tests on two different SDLOs in several configurations with connected and unconnected ends. Finally, the skill is also exploited in the robotized assembly of a motorbike braking system.

Teaching Industrial Robotics in Higher Education with the Visual-based Android Application Hammer

Robotics is a demanding subject in higher education. Learning methodologies need to be updated to make use of the new technologies. This paper presents a new methodology for teaching industrial robotics programming using a visual interface running on Android devices, called Hammer. This tool allows the control and programming of robots via a visual environment based on the Scratch concept. Thanks to it, students can see the practical part of theoretical concepts learned in class and, at the same time, test and generate tasks and paths for industrial robots while learning the basics of robot programming. Students are not supposed to have any knowledge about the target robot programming language, but a basic knowledge of Robotics. This tool has been tested in this paper through four different guided practical exercises. All exercises have been validated through surveys and the results are presented and discussed in the paper.

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode.

The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in C# language in Visual Studio environment, while the program controlling the industrial robot was developed in RAPID language in the RobotStudio environment. Thanks to the development of a digital twin of the real robotic workstation, tests were carried out on the correct functioning of the application in offline mode (without using the real robot). The obtained results were verified in online mode (on a real production station). The developed computer programmes have a modular structure, which makes it possible to easily adapt them to one’s needs. The application allows for changing the parameters of the robot and the parameters of the path drawing. Tests were carried out on the influence of the sampling frequency and the tool diameter on the quality of the reconstructed trajectory of the industrial robot. The results confirmed the correctness of the application. Thanks to the new method of robot programming, it is possible to quickly modify the path by the operator, without the knowledge of robot programming languages. Further research will focus on analyzing the influence of screen resolution and layout scale on the accuracy of trajectory generation.

Real Time Application for Automatic Object and 3D Position Detection and Sorting with Robotic Manipulator

This work deals with the likelihood of merging a 3D sensor into a robotic manipulator, with an objective to automatically detect, track and grasp an object, placing it in another location. To enhance the flexibility and easy functionality of the robot, MATLAB, a versatile and powerful programming language is used to control the robot. For this work, a common industrial task in many factories of pick and place is implemented. A robotic system consisting of an ABB IRB120 robot equipped with a gripper and a 3D Kinect for Windows camera sensor is used. The three-dimensional data acquisition, image processing and some different parameters of the camera are investigated. The information in the image acquired from the camera is used to determine the robot’s working space and to recognize workpieces. This information is then used to calculate the position of the objects. Using this information, an automatic path to grasp an object was designed and developed to compute the possible trajectory to an object in real time. To be able to detect the workpieces, object recognition techniques are applied using available algorithms in MATLAB’s Computer Vision Toolbox and Image Acquisition Toolbox. These give information about the position of the object of interest and its orientation. The information is therefore sent to the robot to create a path through a server-to-client connection over a computer network in real time.

An indirect method of industrial robot programming for machining tasks based on STEP-NC

ABSTRACTSTEP-NC is the next generation of data models between CAD/CAM and CNC systems and it aims at providing a digital thread for CNC manufacturing. After more than a decade of investigation, the STEP-NC technology is still underdeveloped. As the industrial robots are getting more and more capable of taking on machining operations, it is necessary to couple the robot for machining towards new STEP-NC standards. The paper presents an indirect method of industrial robot programming for machining tasks by applying standard ISO 10303–238. A methodology has been established to enable programming and simulation of industrial robots for machining, where information on the machining project is saved at a high information level in the STEP-NC format, which can be readily exchanged between different users for machining, either on machine tools or robots for machining. The methodology also includes the developed robot language converter for STEP-NC programme translation to robot programming language for machining tasks. Developed programming method verification has been performed by simulation in STEP-NC Machine environment using configured virtual robot and after that through several real experiments of drawing the toolpath in 3-axis machining tasks on available vertical articulated robot.

An approach for applying STEP-NC in robot machining

Abstract This paper presents an approach for applying new machining standard ISO 10303-238 in machining operations by using industrial robots. The methodology developed according to this standard is proposed for executing programming, simulation and machining by industrial robots. A detailed description is given of a developed RoboSTEP-NC module which, being part of established methodology, translates the program according to ISO 10303-238 for 3-axis machining into a robot programming language. Programming verification has been realized, first, by simulation on virtual robots configured in STEP-NC Machine environment, and thereafter also by machining on a real available robot after translating machining program by applying the developed RoboSTEP-NC module.

Combining the STEP-NC standard and forward and inverse kinematics methods for generating manufacturing tool paths for serial and hybrid robots

The different forms of data representation have created the need for a common language capable of describing the design, manufacturing and measurement data. ISO 14649, known as STEP-NC, began as an effort to standardise product data exchange typically for computerised numerical control (CNC). However, although there are several studies involving the application of STEP-NC in CNC machines, which use the G-code format (ISO 6983), there is not much work on the application of STEP-NC in generating programmes for industrial robots, which are being increasingly used in production lines, both in quantity and variety. One of the reasons for the difficulty in applying STEP-NC to robots is that the controller needs to receive commands in a specific language imposed by each manufacturer, resulting in a large number of robot programming languages and difficulty in achieving standardisation. This work proposes a procedure that uses forward and inverse kinematics methods applicable to serial and hybrid robots, allowing STEP-NC-compliant information to be received and generates the path along which the robot should move, reducing the time for setup and integration of robots in manufacturing. These paths were tested on three robots with different morphologies, in a virtual environment, confirming the feasibility of the method.

Procedure for definition of end-effector orientation in planar surfaces robot applications

Design of user-friendly and at the same time powerful robot programming methods is the subject of significant efforts undertaken by the international robotics community. For the purpose of facilitating robot programming, with regard to the most common present-day applications in industry, it would be useful to develop programming procedures for frequently used manipulator tasks which could be easily implemented and used as ready-made application software. Important class of industrial robot applications involves end-effector trajectories in planar surfaces. Development of robot programming language procedure intended for determination of object plane normal with respect to frame of interest, as well as programming of end-effector orientation is presented in this paper. This procedure can be used as integral part of task oriented robot programing applications as well as a procedure for explicit programming languages, and it is illustrated in practical example with the robot Lola 15.

The Use of Logic Programming in Task Planning for Robotized Systems

Preparing the task for the robotic system takes several steps. Programming a robot, as the last step, is preceded by the analysis of possible variants of its implementation and trajectory planning. However, the first step is to recognize the task and to split it into fragments, according to activity, without the reference how to realize them. The modern robotics seeks the way to shorten the time to of program preparation, developing the so-called "programming at the task level." It consists in writing a program in a language, which is similar to natural one, describing only the basic operations, without going into the details of their realization the robot (like collision-free path selection or communication with the environment through interfaces and sensors). The outcome is, for example the special voice interfaces used in robotized surgery that allow the surgeon to cooperate with assisting robot without the need to divert their attention from the surgical field. In case of industrial robots, this method is treated as the experimental one and the interest is confined to purely scientific. This article discusses some aspects of this method with respect to the first stages of the task planning. A description of the robot’s workspace and defining the basic steps in a language which is similar to natural one, allows the use of logic programming language to generate pseudocode, which eventually leads to the realization of the task. Every activity could be then divided into elementary steps that must be described directly in the robot programming language. The presented method is currently at the early stage of development and does not allow the direct programming of the robot, but the generated pseudocode could be the basis of a complete program.

Robotic Control & Visualization Toolbox for MATLAB

Industrial robots are used in various application fields and many robot manufacturers are active on the market. In most cases, their software solutions are proprietary and thereby they cannot be used for third party robots. Moreover, the integration of external hard- or software is highly restricted. Long term standardization efforts for robot programming languages, such as the Industrial Robot Language (IRL) and its successor, the Programming Language for Robots (PLR), have been mostly ignored by robot manufacturers. This fact leads to a restriction on the combined usage of robots. Multirobot applications where robots have to interact are usually limited to software solutions and robots of one manufacturer. On the other hand, control design in engineering is often carried out by the usage of Scientific and Technical Computing Environments (SCEs) like MATLAB. The Robotic Control & Visualization (RCV) toolbox for MATLAB tries to close the gap between robot manufacturer-specific software solutions and SCEs. The current version of the RCV toolbox supports a uniform and integrated control development for KUKA and KAWASAKI robots in the MATLAB/Simulink environment. An extension to other robot types is straight forward. Thus, the implementation of heterogeneous multi-robot applications is considerably simplified.

On-line knowledge acquisition and enhancement in robotic assembly tasks

Industrial robots are reliable machines for manufacturing tasks such as welding, panting, assembly, palletizing or kitting operations. They are traditionally programmed by an operator using a teach pendant in a point-to-point scheme with limited sensing capabilities such as industrial vision systems and force/torque sensing. The use of these sensing capabilities is associated to the particular robot controller, operative systems and programming language. Today, robots can react to environment changes specific to their task domain but are still unable to learn skills to effectively use their current knowledge. The need for such a skill in unstructured environments where knowledge can be acquired and enhanced is desirable so that robots can effectively interact in multimodal real-world scenarios. In this article we present a multimodal assembly controller (MAC) approach to embed and effectively enhance knowledge into industrial robots working in multimodal manufacturing scenarios such as assembly during kitting operations with varying shapes and tolerances. During learning, the robot uses its vision and force capabilities resembling a human operator carrying out the same operation. The approach consists of using a MAC based on the Fuzzy ARTMAP artificial neural network in conjunction with a knowledge base. The robot starts the operation having limited initial knowledge about what task it has to accomplish. During the operation, the robot learns the skill for recognising assembly parts and how to assemble them. The skill acquisition is evaluated by counting the steps to complete the assembly, length of the followed assembly path and compliant behaviour. The performance improves with time so that the robot becomes an expert demonstrated by the assembly of a kit with different part geometries. The kit is unknown by the robot at the beginning of the operation; therefore, the kit type, location and orientation are unknown as well as the parts to be assembled since they are randomly fed by a conveyor belt.

PR-PR: cross-platform laboratory automation system.

To enable protocol standardization, sharing, and efficient implementation across laboratory automation platforms, we have further developed the PR-PR open-source high-level biology-friendly robot programming language as a cross-platform laboratory automation system. Beyond liquid-handling robotics, PR-PR now supports microfluidic and microscopy platforms, as well as protocol translation into human languages, such as English. While the same set of basic PR-PR commands and features are available for each supported platform, the underlying optimization and translation modules vary from platform to platform. Here, we describe these further developments to PR-PR, and demonstrate the experimental implementation and validation of PR-PR protocols for combinatorial modified Golden Gate DNA assembly across liquid-handling robotic, microfluidic, and manual platforms. To further test PR-PR cross-platform performance, we then implement and assess PR-PR protocols for Kunkel DNA mutagenesis and hierarchical Gibson DNA assembly for microfluidic and manual platforms.

Techniques for automated testing of Lola industrial robot language Parser

The accuracy of parsing execution directly affects the accuracy of semantic analysis, optimization and object code generation. Therefore, parser testing represents the basis of compiler testing. It should include tests for correct and expected, but also for unexpected and invalid cases. Techniques for testing the parser, as well as algorithms and tools for test sentences generation, are discussed in this paper. The methodology for initial testing of a newly developed compiler is proposed. Generation of negative test sentences by modifying the original language grammar is described. Positive and negative test cases generated by Grow, Purdom's algorithm with and without length control, CDRC-P algorithm and CDRC-P algorithm with length control are applied to the testing of L-IRL robot programming language. For this purpose two different tools for generation of test sentences are used. Based on the presented analysis of possible solutions, the appropriate method can be chosen for testing the parser for smaller grammars with many recursive rules.

PaR-PaR Laboratory Automation Platform

Labor-intensive multistep biological tasks, such as the construction and cloning of DNA molecules, are prime candidates for laboratory automation. Flexible and biology-friendly operation of robotic equipment is key to its successful integration in biological laboratories, and the efforts required to operate a robot must be much smaller than the alternative manual lab work. To achieve these goals, a simple high-level biology-friendly robot programming language is needed. We have developed and experimentally validated such a language: Programming a Robot (PaR-PaR). The syntax and compiler for the language are based on computer science principles and a deep understanding of biological workflows. PaR-PaR allows researchers to use liquid-handling robots effectively, enabling experiments that would not have been considered previously. After minimal training, a biologist can independently write complicated protocols for a robot within an hour. Adoption of PaR-PaR as a standard cross-platform language would enable hand-written or software-generated robotic protocols to be shared across laboratories.

Caesar: an intelligent domestic service robot

In this paper we present Caesar, an intelligent domestic service robot. In domestic settings for service robots complex tasks have to be accomplished. Those tasks benefit from deliberation, from robust action execution and from flexible methods for human---robot interaction that account for qualitative notions used in natural language as well as human fallibility. Our robot Caesar deploys AI techniques on several levels of its system architecture. On the low-level side, system modules for localization or navigation make, for instance, use of path-planning methods, heuristic search, and Bayesian filters. For face recognition and human---machine interaction, random trees and well-known methods from natural language processing are deployed. For deliberation, we use the robot programming and plan language Readylog, which was developed for the high-level control of agents and robots; it allows combining programming the behaviour using planning to find a course of action. Readylog is a variant of the robot programming language Golog. We extended Readylog to be able to cope with qualitative notions of space frequently used by humans, such as "near" and "far". This facilitates human---robot interaction by bridging the gap between human natural language and the numerical values needed by the robot. Further, we use Readylog to increase the flexible interpretation of human commands with decision-theoretic planning. We give an overview of the different methods deployed in Caesar and show the applicability of a system equipped with these AI techniques in domestic service robotics.

Task-oriented motion planning for multi-arm robotic systems

In this paper a task-oriented motion planning approach for general cooperative multi-robot systems is proposed. In order to derive a meaningful task formulation, a taxonomy of cooperative multi-arm systems of industrial interest is devised. Then, a workpiece-oriented general formulation for cooperative tasks is proposed, where the user is asked to specify the motion of the system only at the workpiece level, while the motion of the single arms in the system is computed via kinematic transformations between the relevant coordinate frames. Based on this task formulation, an instructions set is derived to extend classical programming languages for industrial robots to general multi-robot systems. In order to test the approach, a software environment has been built, composed of an interpreter of the language and the motion planning software.

Development of State-Based Squeak and an Examination of Its Effect on Robot Programming Education

Robot programming often sparks students’ interest in programming, but it is not easy for them to program both procedure and reactivity of robot movements that are essential requirements. In this study, we reviewed in detail a new programming language, State-Based Squeak. It allows novice students to implement both procedure and reactivity of robots easily. The effect of this new language on robot programming education was also examined using a group of 28 middle school students. According to the results of analyzing the students’ understanding of programming, reading and programming abilities the group that used State-Based Squeak (the experimental group) showed a higher completion ratio than the other (control) group. The significance of this study is that a robot programming language has been developed that addresses the concepts of both procedure and reactivity in such a way that middle school students can more easily learn how to program robots, something that is often difficult to attempt even for professional programmers.

Reasoning with Qualitative Positional Information for Domestic Domains in the Situation Calculus

In this paper, we present a thorough integration of qualitative representations and reasoning for positional information for domestic service robotics domains into our high-level robot control. In domestic settings for service robots like in the [email protected] competitions, complex tasks such as "get the cup from the kitchen and bring it to the living room" or "find me this and that object in the apartment" have to be accomplished. At these competitions the robots may only be instructed by natural language. As humans use qualitative concepts such as "near" or "far", the robot needs to cope with them, too. For our domestic robot, we use the robot programming and plan language Readylog, our variant of Golog. In previous work we extended the action language Golog, which was developed for the high-level control of agents and robots, with fuzzy set-based qualitative concepts. We now extend our framework to positional fuzzy fluents with an associated positional context called frames. With that and our underlying reasoning mechanism we can transform qualitative positional information from one context to another to account for changes in context such as the point of view or the scale. We demonstrate how qualitative positional fluents based on a fuzzy set semantics can be deployed in domestic domains and showcase how reasoning with these qualitative notions can seamlessly be applied to a fetch-and-carry task in a [email protected] scenario.

J-Automatic Welding Robot Software Design

In this paper, according to nuclear reactor pressure vessel for the J-groove type, independent research and development of a special groove in the J-automatic control system program. This paper introduces a PC-based industrial computer as the host operating system and the functional modules are described respectively, by comparison of a variety of robot programming languages​​, ultimately to achieve the goal of the program using the Visual C++ MFC platform. The system has a friendly interface, which can be easily set to display, on-line adjustment and automatic welding for the preservation, through simulation experiment with industrial PC connection to verify that the entire system software design is reasonable, functional and meet the actual needs of production.

Method for Planning Multitasking Robot Application Programs in RAPID

In order to set up well-structured multitasking robot application programs careful planning is required. Robot programming languages (e.g. Karel, RAPID, Melfa, SimPro, etc.) vary from robot to robot constructor. General planning tools used in software development (e.g. UML, IDEF, etc.) require adequate professional skills and a special way of thinking such that robot programmers to apply and adapt them to the specificity of each robot programming language. Customized and intuitive planning tools of robot applications with regard to each particular programming language seem to be preferred by ordinary robot programmers and operators when facing with the development of complex robot tasks. This paper introduces such a tool in relation to the RAPIDTM programming language, specific to ABB robot models. Its effectiveness is revealed in a case study.