“…The workpiece in the drawing process is a 3-D-printed rabbit model. The optimal robot base placement relative to the 3-D object is computed offline using the optimization-based algorithm proposed in [42]. The actual robot base placement in the experimental setup is determined using a calibration procedure before the experiment can start, see Fig.…”
In industrial applications, planning and executing robot motions are crucial steps for manufacturing processes. Following the trend for customization, more flexible production systems are needed to quickly adapt the planned robot motion to new user inputs. In this work, a user-defined 2-D input pattern has to be drawn by a robot on a given 3-D object in an automated workflow. For this, two projection methods to map the 2-D input pattern to the 3-D object are presented, and robot trajectories are automatically generated based on the result of the projection methods. Furthermore, two control concepts, i.e., a pure motion control and a hybrid force/motion control, are investigated and validated by experimental results. In addition, a precise force estimation is performed to guarantee a constant normal contact force during the drawing process. The proposed automated workflow is applicable to various industrial processes, e.g., spray painting, cutting, and engraving, and provides an easy way to plan and execute robot motions based on user inputs.
“…The workpiece in the drawing process is a 3-D-printed rabbit model. The optimal robot base placement relative to the 3-D object is computed offline using the optimization-based algorithm proposed in [42]. The actual robot base placement in the experimental setup is determined using a calibration procedure before the experiment can start, see Fig.…”
In industrial applications, planning and executing robot motions are crucial steps for manufacturing processes. Following the trend for customization, more flexible production systems are needed to quickly adapt the planned robot motion to new user inputs. In this work, a user-defined 2-D input pattern has to be drawn by a robot on a given 3-D object in an automated workflow. For this, two projection methods to map the 2-D input pattern to the 3-D object are presented, and robot trajectories are automatically generated based on the result of the projection methods. Furthermore, two control concepts, i.e., a pure motion control and a hybrid force/motion control, are investigated and validated by experimental results. In addition, a precise force estimation is performed to guarantee a constant normal contact force during the drawing process. The proposed automated workflow is applicable to various industrial processes, e.g., spray painting, cutting, and engraving, and provides an easy way to plan and execute robot motions based on user inputs.
“…The researchers optimized the location of the robot to generate maximum task-space velocity with minimum joint velocities [23]. For robot-to-workpiece placement for large-scale welding systems [24], the authors generated a kinematic performance map based on a kinetostatic condition index that was used to optimize robot configurations in a polishing application [25], introduced a custom index for robot-based placement optimization demonstrated in a trim application in shoe manufacturing [26], and optimized a workpiece placement for the robotic operation in challenging manufacturing tasks [27,28] and surface finishing [21,29]. An interesting new optimization approach was also introduced to maximize the available velocities of the end-effector during a task execution of path following in robot machining called the decomposed twist feasibility method [30].…”
Robot workpiece machining is interesting in industry as it offers some advantages, such as higher flexibility in comparison with the conventional approach based on CNC technology. However, in recent years, we have been facing a strong progressive shift to custom-based manufacturing and low-volume/high-mix production, which require a novel approach to automation via the employment of collaborative robotics. However, collaborative robots feature only limited motion capability to provide safety in cooperation with human workers. Thus, it is highly necessary to perform more detailed robot task planning to ensure its feasibility and optimal performance. In this paper, we deal with the problem of studying kinematic robot performance in the case of such manufacturing tasks, where the robot tool is constrained to follow the machining path embedded on the workpiece surface at a prescribed orientation. The presented approach is based on the well-known concept of manipulability, although the latter suffers from physical inconsistency due to mixing different units of linear and angular velocity in a general 6 DOF task case. Therefore, we introduce the workpiece surface constraint in the robot kinematic analysis, which enables an evaluation of its available velocity capability in a reduced dimension space. Such constrained robot kinematics transform the robot’s task space to a two-dimensional surface tangent plane, and the manipulability analysis may be limited to the space of linear velocity only. Thus, the problem of physical inconsistency is avoided effectively. We show the theoretical derivation of the proposed method, which was verified by numerical experiments.
“…The researchers optimized the location of the robot to generate maximum task-space velocity with minimum joint velocities [23]. For robot-to-workpiece placement for large-scale welding systems [24], the authors generated a kinematic performance map based on a kinetostatic condition index that was used to optimize robot configurations in a polishing application [25], introduced a custom index for robot-based placement optimization demonstrated in a trim application in shoe manufacturing [26], and optimized a workpiece placement for the robotic operation in challenging manufacturing tasks [27,28] and surface finishing [21,29]. An interesting new optimization approach was also introduced to maximize the available velocities of the end-effector during a task execution of path following in robot machining called the decomposed twist feasibility method [30].…”
Robot workpiece machining is interesting in industry since it offers some advantages, such as higher flexibility in comparison with the conventional approach based on the CNC technology. However, in recent years we have been facing a strong progressive shift to custom based manufacturing and low volume/high mix production, which require a novel approach to automation by the employment of collaborative robotics. However, collaborative robots feature only limited motion capability, to provide safety in cooperation with human workers. Thus, it is highly necessary to perform more detailed robot task planning to ensure its feasibility and optimal performance. In this paper, we deal with the problem of studying kinematic robot performance in the case of such manufacturing tasks, where the robot tool is constrained to follow the machining path embedded on the workpiece surface at a prescribed orientation. The presented approach is based on the well-known concept of manipulability, although the latter suffers from physical inconsistency due to mixing different units of linear and angular velocity in a general 6 DOF task case. Therefore, we introduce the characteristics of the workpiece surface constraint in the robot kinematic analysis, that enables evaluation of its available velocity capability in a reduced dimension space. Such constrained robot kinematics transform the robot`s task space to a two-dimensional surface tangent plane, and the manipulability analysis may be limited to the space of linear velocity only. Thus, the problem of physical inconsistency is avoided effectively. We show the theoretical derivation of the proposed method, which was verified by numerical experiments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.