Integrated architecture for industrial robot programming and control

As robot control systems are traditionally closed, it is difficult to add supplementary intelligence. Accordingly, as based on a new notion of user views, a layered system architecture is proposed. Bearing in mind such industrial demands as computing efficiency and simple factory-floor operation, the control layers are parameterized by means of functional operators consisting of pieces of compiled code that can be passed as parameters between the layers. The required interplay between application-specific programs and built-in motion control is thereby efficiently accomplished. The results from experimental evaluation and several case studies suggest the architecture to be very useful also in an industrial context.     

Combining active learning and reactive control for robot grasping

Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp requires learning about the object as a whole, the execution only needs to reactively adapt to the context close to the grasp’s location. We propose a hierarchical controller that reflects the structure of these two sub-problems, and attempts to learn solutions that work for both. A hybrid architecture is employed by the controller to make use of various machine learning methods that can cope with the large amount of uncertainty inherent to the task. The controller’s upper level selects where to grasp the object using a reinforcement learner, while the lower level comprises an imitation learner and a vision-based reactive controller to determine appropriate grasping motions. The resulting system is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshaping the hand depending on the object’s geometry. The system was evaluated both in simulation and on a real robot.     

Efficient robot arm modeling for computer control

This article presents a comparative study of the required number of arithmetic operations necessary for computing robot arm models using the Denavit-Hartenberg symbolic notation and a proposed one. The proposed notation is based on the idea of describing the motion of a robot joint by a pair matrix and the geometry of a link by a shape matrix. This notation needs the use of two coordinate systems for each joint or link. The results prove that the proposed notation reduces the computation time of robot models. For a 6-degrees of freedom robot arm, the computation times of kinematic position, velocity, and dynamic models are reduced respectively by 20%, 5%, and 2%, respectively. The two notations have the same effects on computing the inverse models. © 1993 John Wiley & Sons, Inc.     

机器人控制软件的图形化编程系统设计

提出了一种基于图形化编程的机器人软件系统的总体结构设计,包括软件结构的总体设计思路,主要处理从流程图式的图形化结构到类C语言的转换,再将这种类C语言翻译转换为下位机所能识别的目标代码,经过串口通讯下载到下位机中,完成上位机和下位机的信息交互.提出的设计方法和思路已经过严格的测试和检测,实际应用表明,该系统具有较好的灵活性,稳定性和实用性.     

Error management for robot programming

Reliability is a serious problem in computer controlled robot systems. Although robots serve successfully in relatively simple applications such as painting and spot welding, their potential in areas such as automated assembly is hampered by the complexity of programming. A program for assembling parts may be logically correct, execute correctly on a simulator, and even execute correctly on a robot most of the time, yet still fail unexpectedly in the face of real world uncertainties. Recovery from such errors is far more complicated than recovery from simple controller errors, since even expected errors can manifest themselves in unexpected ways. In this paper we present a novel approach for improving robot reliability. Instead of anticipating errors, we use knowledge-based programming techniques so that the robot can autonomously exploit knowledge about its task and environment to detect and recover from failures. We describe a system that we have designed and constructed in our robotics laboratory.     

Dynamic modeling and control of hopping robot in planar space

The paper presents two-mass inverted pendulum (TMIP) model and its control scheme for hopping robot. Unlike the conventional spring-loaded inverted pendulum (SLIP) model, the proposed TMIP model is able to provide the functions of energy storing and releasing by using a linear actuator. Also it becomes more accurate comparing to the conventional SLIP model by taking the foot mass into consideration. Furthermore how to determine both takeoff angle and velocity for hopping is analytically suggested to accomplish the desired stride and height of hopping robot. The control method for the TMIP model is also presented in the paper. Finally, the effectiveness of the proposed model and control scheme is verified through the simulation.     

A one linear actuator hopping robot: modeling and control

This paper addresses the modeling and control design of a one linear actuator hopping robot. The robot consists of a body and a leg, which are in contact with a sufficiently wide horizontal ground surface; both are fixed rigidly. Force actuation affects the angle of the body by a force couple that arises due to the mass of the body, as well as the length of the leg; hence both the angular velocity of the body and height of a jump can be controlled by only one actuator. Since the aim of this study is to achieve continuous hopping motion while keeping the system as simple as possible, an ON-OFF actuator is employed. Hence, we consider utilizing the thrust timing of the actuator—when robot is in the stance phase—to control the gait. For better stability of the hopping motion, optimization of mechanical parameters was made possible by evaluating the numerically obtained transition map, which contains a transition from one standard position to the next. The system is considered as a discrete system, in which one cycle of motion is regarded as one sampling interval. Finally, a control system was designed in which, by simulation, the continuous hopping gait was realized.     

Graphical simulation for sensor based robot programming

The programming of robots is slowly evolving from traditional teach pendant methods to graphical Off-Line Programming (OLP) methods. Graphical simulation tools, such as OLP, are very useful for developing and testing robot programs before they are run on real industrial equipment. OLP systems are also used to develop task level programs. Traditional OLP systems, however, suffer from the limitations of using only position control which does not account for inherent robot inaccuracies and dynamic environments. This paper describes our work on improving and supplementing traditional position control programming methods. A baseline OLP system was implemented at NIST's Automated Manufacturing Research Facility (AMRF). Experience gained in implementing this system showed that an effective OLP system must accurately simulate the real world and must support sensor programming to compensate for real-world changes that cannot be simulated. The developed OLP geometric world model is calibrated using robot mounted ultrasound ranging sensors. This measurement capability produces a baseline geometric model of relatively good static accuracy for off-line programming. The graphical environment must also provide representations of sensor features. For this specific application, force is simulated in order to include force based commands in our robot programs. These sensor based programs are able to run reliably and safely in an unpredictable industrial environment. The last portion of this paper extends OLP and describes the functionality of a complete system for programming complex robot tasks.     

A system for programming and controlling sensor-based robot manipulators

This paper describes an approach to programming and controlling robot manipulators which facilitates the use of sensory information. Robot actions are specified by declaring software servo processes which control the robot's various degrees of freedom. These servo processes can involve position, orientation, force, and torque information from the robot itself, or data from external sensors. Robot tasks are programmed by dynamically modifying the servo processes or by changing set points to these processes. Condition monitors, which have access to program and sensory information, detect the completion of program steps.     

Agent-oriented software patterns for rapid and affordable robot programming

Robotic systems are often quite complex to develop; they are huge, heavily constrained from the non-functional point of view and they implement challenging algorithms. The lack of integrated methods with reuse approaches leads robotic developers to reinvent the wheel each time a new project starts. This paper proposes to reuse the experience done when building robotic applications, by catching it into design patterns. These represent a general mean for (i) reusing proved solutions increasing the final quality, (ii) communicating the knowledge about a domain and (iii) reducing the development time and effort. Despite of this generality, the proposed repository of patterns is specific for multi-agent robotic systems. These patterns are documented by a set of design diagrams and the corresponding implementing code is obtained through a series of automatic transformations. Some patterns extracted from an existing and freely available repository are presented. The paper also discusses an experimental set-up based on the construction of a complete robotic application obtained by composing some highly reusable patterns.     

Virtual-base modeling and coordinated control of a dual-arm space robot for target capturing and manipulation

Multibody System Dynamics - A dual-arm space robotic system has large potential in on-orbit servicing missions, such as satellite repairing, large space structure construction and space debris...     

A three-dimensional machine-vision approach for automatic robot programming

This research investigates a novel robot-programming approach that applies machine-vision techniques to generate a robot program automatically. The hand motions of a demonstrator are initially recorded as a long sequence of images using two CCD cameras. Machine-vision techniques are then used to recognize the hand motions in three-dimensional space including open, closed, grasp, release and move. The individual hand feature and its corresponding hand position in each sample image is translated to robot's manipulator-level instructions. Finally a robot plays back the task using the automatically generated program.A robot can imitate the hand motions demonstrated by a human master using the proposed machine-vision approach. Compared with the traditional leadthrough and structural programming-language methods, the robot's user will not have to physically move the robot arm through the desired motion sequence and learn complicated robot-programming languages. The approach is currently focused on the classification of hand features and motions of a human arm and, therefore, is restricted to simple pick-and-place applications. Only one arm of the human master can be presented in the image scene, and the master must not wear long-sleeved clothes during demonstration to prevent false identification. Analysis and classification of hand motions in a long sequence of images are time-consuming. The automatic robot programming currently developed is performed off-line.     

Dual-coding representations for robot vision programming in Tekkotsu

We describe complementary iconic and symbolic representations for parsing the visual world. The iconic pixmap representation is operated on by an extensible set of “visual routines” (Ullman, 1984; Forbus et al., 2001). A symbolic representation, in terms of lines, ellipses, blobs, etc., is extracted from the iconic encoding, manipulated algebraically, and re-rendered iconically. The two representations are therefore duals, and iconic operations can be freely intermixed with symbolic ones. The dual-coding approach offers robot programmers a versatile collection of primitives from which to construct application-specific vision software. We describe some sample applications implemented on the Sony AIBO. David S. Touretzky is a Research Professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. He earned his B.A. in Computer Science from Rutgers University in 1978, and his M.S. (1979) and Ph.D. (1984) in Computer Science from Carnegie Mellon. Dr. Touretzky’s research interests are in computational neuroscience, particularly representations of space in the rodent hippocampus and related structures, and high level primitives for robot programming. He is presently developing an undergraduate curriculum in cognitive robotics based on the Tekkotsu software framework described in this article. Neil S. Halelamien earned a B.S. in Computer Science and a B.S. in Cognitive Science at Carnegie Mellon University in 2004, and is currently pursuing his Ph.D. in the Computation & Neural Systems program at the California Institute of Technology. His research interests are in studying vision from both a computational and biological perspective. He is currently using transcranial magnetic stimulation to study visual representations and information processing in visual cortex. Ethan J. Tira-Thompson is a graduate student in the Robotics Institute at Carnegie Mellon University. He earned a B.S. in Computer Science and a B.S. in Human-Computer Interaction in 2002, and an M.S. in Robotics in 2004, at Carnegie Mellon. He is interested in a wide variety of computer science topics, including machine learning, computer vision, software architecture, and interface design. Ethan’s research has revolved around the creation of the Tekkotsu framework to enable the rapid development of robotics software and its use in education. He intends to specialize in mobile manipulation and motion planning for the completion of his degree. Jordan J. Wales is completing a Master of Studies in Theology at the University of Notre Dame. He earned a B.S. in Engineering (Swarthmore College, 2001), an M.Sc. in Cognitive Science (Edinburgh, UK, 2002), and a Postgraduate Diploma in Theology (Oxford, UK, 2003). After a year as a graduate research assistant in Computer Science at Carnegie Mellon, he entered the master’s program in Theology at Notre Dame and is now applying to doctoral programs. His research focus in early and medieval Christianity is accompanied by an interest in medieval and modern philosophies of mind and their connections with modern cognitive science. Kei Usui is a masters student in the Robotics Institute at Carnegie Mellon University. He earned his B.S. in Physics from Carnegie Mellon University in 2005. His research interests are reinforcement learning, legged locomotion, and cognitive science. He is presently working on algorithms for humanoid robots to maintain balance against unexpected external forces.     

ADA: A language for robot programming?

Robot programming languages are emerging from their experimental stage and entering an assessment phase. Their main features are illustrated and a parallel with ADA is proposed. The comparison is positive for ADA, in the sense that ADA provides most of the required capabilities. The ability of reasoning on object models and taking decisions will play an increasing role in the future. In this case the role of ADA would possibly change and its interest as robot programming language decrease.     

A sensor based technique for automated robot programming

This paper presents the concept of integrating a tactile sensor based, automated part measuring technique and a CAD programming scheme to perform off-line programming for a welding robot. The techniques used to achieve data flow throughout the production process include coordinate system referencing, interactive programming, tactile sensing seam tracing, and coordinate transformation. Together they form the backbone of the idea of integrating measurement and production operations where continuous path geometrical control is required. Techniques for referencing a workpiece with respect to different handling devices, and positioning the part on a suitable fixture, form an important portion of this work. Consequently, the positioning aspect and the data transfer capability between stages in the production process are highlighted. The welding of lap-joint type seams serves as an example. The integration of automated measurement and off-line robot programming actually constitutes a flexible manufacturing system operation that is capable of assuring the required process control. In this regard, linking a robot with an automated part measuring technique forms an important step towards upgrading a robot device from a difficult to program unit to a unit with a high degree of flexibility with rapid, convenient automatic contour programming.     

Friction modeling and compensation for an industrial robot

This article describes the detailed friction modeling of the General Electric GP132 industrial robot. Friction is an area whose importance is often discounted in the development of control systems because it is thought to be insignificant or unmodelable. This work demonstrates that friction does have a predictable structure, and that significant performance improvement can be realized through its proper compensation. Experiments performed to determine the static, Coulomb, and viscous friction of the GP132 are presented. In addition to these components, the robot is shown to have significant gravity load-dependent and position-dependent friction. The accuracy of the friction models are verified through several experiments and are shown to be considerably better than previously formulated models.     

Adaptive robot training by programming and guiding

The simplest and most common robot training method is operator-guiding, but this method is inflexible and limited to fixed sequences. Explicit textual robot programming has enjoyed many advances in the last decade and current research implementations are powerful. But explicit programming is complex and programmers must be specially trained. Mixed systems attempt to unite the facility of guiding with the power of programming. However, existing mixed systems exhibit mismatches in the interaction between programming and guiding, because they ignore underlying dependencies between the programmer and operator. An operator may (1) be given insufficient visual cues; (2) lack the required dexterity; or (3) be required to add unforeseen movement sequences to the program. This paper presents the design and implementation of ART (adaptive robot trainer), a prototypical mixed robot training system that eliminates or corrects deficiencies in visual cues and dexterity, and additionally improves the guiding and programming components. Mixed systems and assembly tasks are analysed, to give an effective representation of task state, which in turn motivates the design of ART's language to automate much of the program-guiding interaction. The language allows the programmer to express assembly operations and object-feature relationships in a natural way while providing the system with the information necessary to maintain the task state. The representation also enables the correction of guiding errors, flexibility in the guiding protocol and the generation of meaningful messages to prompt operator actions. These principles in the design and implementation pave the road to more instructable, capable robots.Now at Bell Northern Research, Ottawa.     

Measuring and modeling programming experience

Programming experience is an important confounding parameter in controlled experiments regarding program comprehension. In literature, ways to measure or control programming experience vary. Often, researchers neglect it or do not specify how they controlled for it. We set out to find a well-defined understanding of programming experience and a way to measure it. From published comprehension experiments, we extracted questions that assess programming experience. In a controlled experiment, we compare the answers of computer-science students to these questions with their performance in solving program-comprehension tasks. We found that self estimation seems to be a reliable way to measure programming experience. Furthermore, we applied exploratory and confirmatory factor analyses to extract and evaluate a model of programming experience. With our analysis, we initiate a path toward validly and reliably measuring and describing programming experience to better understand and control its influence in program-comprehension experiments.