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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
teaching-by-showing programming a programming technique in which the operator guides the manipulator manually or by means of a teach pendant along the desired motion path. During this movement, the data read by joint position sensors (all robots are equipped with joint position sensors) are stored. During the execution of the motion (playing back), these data are utilized by the joint drive servos. Typical applications of this kind of programming are spot welding, spray painting, and simple palletizing. Teaching-by-doing does not require special programming skill and can be done by a plant technician. Each industrial robot is equipped with these capabilities. Also called teaching-by-doing. team decision decision taken independently by several decision makers being in charge of a given process (or a decision problem) and forming a team, i.e., contributing to a commonly shared goal. teaser coil See teaser transformer.
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Published in Phillip A. Laplante, Dictionary of Computer Science, Engineering, and Technology, 2017
teaching-by-showing programming a programming technique in which the operator guides the manipulator manually or by means of a teach pendant along the desired motion path. During this movement the data read by joint position sensors (all robots are equipped with joint position sensors) are stored. During the execution of the motion (playing back) these data are utilized by the joint drive servos. Typical applications of this kind of programming are spot welding, spray painting, and simple palletizing. Teaching-by-doing does not require special programming skill and can be done by a plant technician. Each industrial robot is equipped with these capabilities. Also called teaching-by-doing.
Multifingered Hand Kinematics
Published in Richard M. Murray, Zexiang Li, S. Shankar Sastry, A Mathematical Introduction to Robotic Manipulation, 2017
Richard M. Murray, Zexiang Li, S. Shankar Sastry
A second disadvantage with traditional robot manipulators is that for a given gripper, only a small class of objects can be grasped. A parallel jaw gripper, for example, is very effective at grasping objects which have parallel faces. It cannot, however, be used to “stably” grasp a tetrahedron. This limitation is sometimes overcome by equipping the robot arm with a tool changer, which allows different grippers to be attached to a robot in an efficient fashion. While this effectively extends the class of objects which can be lifted, it does not specifically address the fine motion problem.
A comparative study of manipulator teleoperation methods for debris retrieval phase in nuclear power plant decommissioning
Published in Advanced Robotics, 2023
Naoki Mizuno, Yuichi Tazaki, Tatsuya Hashimoto, Yasuyoshi Yokokohji
In the teaching-based method, the manipulator is operated by replaying a pre-programed teaching data. This method is often employed in periodic inspections at nuclear power plants and industrial robots. An extensive survey of recent teaching techniques is given in Ref. [16]. A scene of generating teaching data using a simulator is shown in Figure 6. The environment model and the robot model are displayed on the simulator, and an arbitrary manipulator position is generated using the manual teleoperation described previously and registered as a waypoint. The person who generates the teaching data confirms that interference with the environment does not occur at each waypoint as well as while moving between waypoints. If interference occurs between waypoints, interference is resolved by either adjusting the pose of existing waypoints or inserting a new waypoint in between. Since teaching requires manual editing and inspection by a human operator, it generally takes a large amount of time to generate a complete set of teaching data.
Improving processes and ergonomics at air freight handling agents: a case study
Published in International Journal of Logistics Research and Applications, 2023
Heiko Diefenbach, Nathalie Erlemann, Alexander Lunin, Eric H. Grosse, Kai-Oliver Schocke, Christoph H. Glock
A manipulator is the part of a robot that performs movements, interacts with objects and thus does physical work (Lewis, Dawson, and Abdallah 2003), like, for example, an industrial robotic arm. In addition to these fully automated manipulators, there are manipulators that are actively controlled by workers. The latter are also referred to as cobots (an abbreviation of ‘collaborative robot’) (Krüger, Lien, and Verl 2009). Most cobots resemble (jib) cranes or crane-like robotic arms. The worker operates the cobot directly on the arm by specifying a movement, i.e. guiding the arm. The cobot senses the movement and supports it with an actively applied force (Peshkin et al. 2001; Krüger, Lien, and Verl 2009). Krüger, Lien, and Verl (2009) report that this can reduce the manual force required to move a load by a factor of ten and more. Cobots can be designed to support movements in all spatial dimensions within their reach. They are stationary however. Due to the intuitive control, they are less sluggish than cranes, which can lead to a lower reduction in productivity or even an increase in productivity, as Gil-Vilda et al. (2017) report. This makes cobots better suited to handle small-piece freight. Especially cardboard boxes could be handled very efficiently if the cobot is equipped with a vacuum gripper. However, the application of cobots is limited to the build-up and break-down of air freight pallets, since the jib is too cumbersome to move within containers.
Image space trajectory tracking of 6-DOF robot manipulator in assisting visual servoing
Published in Automatika, 2022
Megha G. Krishnan, Ashok Sankar
The requirement for automated industrial operations is driving up the demand for robots in manufacturing industries. Industrial robots, mainly robot manipulators play a key role in industrial automation. Robotic manipulators have been used in various industrial applications like spot welding, material handling, pick & place and many more. It requires high endurance, speed and meticulousness. However, the application of manipulators in industries is limited by their lack of intelligence to take decisions. To overcome this problem, a vision sensor is integrated into the robot control systems. This provides a better operation and aids the robot to navigate the landscape and avoid collisions. In visual servoing, the data acquired from the vision sensors are used to control the motion of a robot. Mathematically, the error between the desired and actual visual features is minimized. On the other hand, the loss of data while projecting the 3D information onto a 2D image plane in the camera is a challenge in vision-based control. Moreover, the non-linearities and complex structure of a manipulator robot make the problem more complex [1].