Robot Companion for Industrial Process Monitoring Based on Virtual Fixtures

Abstract

In this paper, the use of a monitoring companion is proposed to more efficiently collect the process information during the tuning of industrial systems, and therefore to assist the system integrator in the optimization process for complex robotic installations. The monitoring companion consists of an industrial manipulator (monitoring robot) and a Unity-ROS framework for controlling it. We discuss in this paper the features that allow the solution to be re-used in different industrial application thanks to its compatibility with ROS. In particular, we present the approach based on admittance virtual fixtures and how we use such abstraction to track a moving target (it could be the end-effector of another robot for example). Moreover, the concept of varying compliance is introduced as a way to influence the motion of the monitoring robot on the virtual fixtures in the presence of obstacles. The experiments have been conducted in simulation as well as on real hardware to test the accuracy of the system at respecting the virtual fixtures with both a static and a moving monitoring target, although in the current implementation the varying compliance was not included in the experiments.

Method for generating real-time interactive virtual fixture for shared teleoperation in unknown environments

“…Teleoperation extends human sensing and manipulating capabilities to remote and hazardous environments where autonomous or preprogrammed task execution is not feasible, owing to the uncertainties in the environments and the complexity of given tasks. With the recent advancements of robot hardware, communications, and control technologies, there has been renewed interest in teleoperation, and its application areas have been expanded into medicine (Su et al, 2020), biological cell manipulation (Mohand-Ousaid et al, 2020), inspection and maintenance of wind turbines, oil and gas platforms (Lee et al, 2020), industrial process-monitoring (Sita et al, 2018), dexterous manipulation (Fishel et al, 2020), learning from demonstration (Latifee et al, 2020), and soft growing robot (Stroppa et al, 2020).…”

“…Additionally, VFs associated with RR tools help prevent collision among multiple RRs operating in proximity (Marinho et al, 2020; Moccia et al, 2020). They also help RRs perform complicated motions, such as knot-tying (Chen et al, 2016), suturing (Marinho et al, 2020), and manufacture monitoring (Sita et al, 2018). Moreover, multiple preliminary demonstrations can be used to generate VFs for subsequent repeated tasks (Papageorgiou et al, 2020; Pérez-del Pulgar et al, 2016; Raiola et al, 2018).…”