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Programming for Intelligent Robot Systems
Published in Spyros G. Tzafestas, Intelligent Robotic Systems, 2020
Programming robots off-line is appealing. A Delphi forecast of markets and technology published in 1982 projected an increase in off-line programming from an estimated 6% in 1981 to 28% in 1990 (Smith and Wilson, 1982). Robots could do useful work for the period of time in which new programs are developed. Off-line programming allows the robot program to be developed even before the parts that the robot will manipulate are available. This makes it possible to design better parts if simulation of the assembly process shows that some changes are needed. An additional advantage is that off-line programming could be performed in an environment more suited to programming than the factory floor. Larger computers could be used because they will not be tied to a single robot. This opens the area of robot programming to applications of artificial intelligence techniques (Brady, 1985; Kempf, 1985).
Automatic Error Detection and Recovery
Published in Ulrich Rembold, Robot Technology and Applications, 2020
A third area of concern in robot programming is the need to do as much work as possible off-line—that is, “off-line with respect to the robot,” not “off-line with respect to the computer,” as commonly intended in computer science. To control development costs, many robot users use robot programming languages to develop robot task descriptions off-line. When possible they test their programs offline using simulators to further avoid tying up production line robots with nonproduction work. Off-line programming also allows robot programs to be developed before the parts that the robot will manipulate are available. This makes it possible to modify the design of the parts to ease the assembly process. Another advantage is that off-line programming can be performed in an environment more suited to programming than the factory floor. Larger computers can be used because they are not tied to a single robot. This opens the area of robot programming to applications of artificial intelligence techniques. Unfortunately, programs developed off-line tend to be unreliable and error prone. Most of the problems come from the lack of real sensor data, since sensor interaction can only be simulated [17]. Despite the potential problems, off-line programming is here to stay. A Delphi forecast of markets and technology published in 1982 [5] projects an increase in off-line programming from an estimated 6% in 1981 to 28% in 1990. Integration of multiple sensors and multiple robots into a total manufacturing system will certainly require powerful off-line programming capabilities.
Hiding task-oriented programming complexity: an industrial case study
Published in International Journal of Computer Integrated Manufacturing, 2023
Enrico Villagrossi, Michele Delledonne, Marco Faroni, Manuel Beschi, Nicola Pedrocchi
The robot-oriented programming languages are currently the most widespread for industrial applications, even if offline programming environments more and more often flank them. Thanks to accurate robotic cell modelling, the development and the simulation of the robot programs, with offline tools, before on-site testing allows saving time (Pan et al. 2012). Integrating offline programming environments with CAD systems has also led to the automatic generation of the robot part program (Castro et al. 2019). Classic use is for continuous processes such as machining, painting and arc welding applications (i.e., a vast number of via-points are generated by a CAD/CAM system and interpolated by the robot). Offline programming can partially generate collision-free trajectories for a given planning environment. More frequently, they are used to check and highlight the presence of collisions between the robot and the environment. As for robot-oriented programming languages, every robot manufacturer developed its offline programming environment, such as ABB RobotStudio, Kuka. Sim, Motoman MotSim, Fanuc RoboGuide. Nevertheless, an accurate 3D model of the cell is not always available, and the construction of the cell can bring inaccuracies w.r.t. the original design. Hence, adopting the traditional drive-through programming is frequent during the commissioning phase to re-teach trajectories via-points.
Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace
Published in International Journal of Computer Integrated Manufacturing, 2020
Ali Ahmad Malik, Tariq Masood, Arne Bilberg
Once the design-decisions are finalised, the robots used in HRC are programmed either by online robot programming tools (i.e. teach pendant) or offline programming tools (i.e. graphical software). Offline programming tools are both brand specific (e.g. Robot Studio, KUKA) and some are general purpose (Robot Expert, Process Simulate, DELMIA). When it comes to digital modelling of human work-tasks, the simulation of digital human model has been a challenging task and is often not integrated into manufacturing virtual simulations. If used, the quality of the digital human model is far from reality. This keeps an important aspect of a production system (i.e. humans) out of the loop in the planning phase. Most often, humans-as end users only evaluate the new manufacturing system in a physical-simulation late when development work is done.
Operation of a haptic interface for offline programming of welding robots by applying a spring-damper model
Published in International Journal of Computer Integrated Manufacturing, 2019
Angel Sanchez-Diaz, Ulises Zaldivar-Colado, J. Alfonso Pamanes-Garcia, Xiomara Zaldivar-Colado
The programming of a welding robot by applying typical procedures often requires an important amount of time of the programmer by using the robot. During this time, the robot is unproductive and consequently, a programming session becomes expensive. Accordingly, significant research efforts have been addressed in research organisations to develop simple and inexpensive programming systems for welding robots. A typical method of programming a robot is based on an online process that uses a teach-pendant attached to the robot’s control. The programmer activates buttons of the teach-pendant for moving the robot and guiding the torch in such a way that this tool touches a sample of the desired path-points. Then, the corresponding taught torch poses are saved and computationally processed to finally obtain the whole desired sequence of robot’s motions. Usually, this kind of programming involves manual repetitions, which are risky, slow and harmful for the production process (Pan et al. 2012). On the other hand, there are those researches oriented to offline programming (OLP) systems based in a VE of a workstation where a 3D model of the robot is incorporated. In this environment, the user specifies by simulation the required welding paths without interfering in the production process (Dong, Li, and Teng 2007). This graphic simulation is carried out within a VE structured by 3D objects that are modelled in computer-aided design (CAD) software packages.