The MANTIS project is concerned with predictive maintenance on the basis of big data streams from large (industrial) operations. At the end of the processing pipe line, planning suggestions for maintenance actions will be the result. Usually, maintenance is performed by human operators.
However, with current developments in machine learning, AI and robotics, it becomes interesting to see what type of ‘corrective actions’ in maintenance could be performed by industrial service robots.
In industrial production lines it is common to observe fairly short times between failure, especially in long chains. Whereas individual components are often designed to function extremely well, for instance under a regime of ‘zero-defect manufacturing’, the performance of the line as a whole may be disappointing. What is more, the actions performed by human operators to solve the problems may be very mundane and simple, such as removing dirt due to fouling or lubricating critical components. With the current advances in robot hardware and software technology, it becomes increasingly attractive to automate such maintenance actions. Whereas maintenance in the form of module- or part replacement are too difficult for current state-of-the-art robotics, cleaning and tidying is definitely possible.
With this application domain in mind, a laboratory setup was designed for quickly developing a robotic maintenance task for the purpose of demonstration by a master student team (Francesco Bidoia, Rik Timmers, Marc Groefsema) under guidance of a PhD student (Amir Shantia). We were able to realize a rapid configuration of our existing mobile robot platform to realize simple cleaning and tidying actions, similar to what is needed in basic industrial maintenance tasks. The demonstration involves speech control, navigational autonomy, work piece approach and dynamic reactivity to three object types, using tool switching. Objects are considered to be either a) untouchable, or b) removable by hand, or to consist, c) of small fragments (cf. ‘dirt’) that needs to be brushed away. In three weeks, a full demonstration could be developed by the student team, using a mobile robot with a single arm that was designed earlier, for Robocup@Home tasks:
The robot in our demonstration uses the light-weight carbon-fiberarm by Kinova (http://www.kinovarobotics.com/), a self-made transport base, standard Kinect sensors (for generating 3D point clouds) and digital cameras for vision. Programming was done using the ROS environment, with a pre-existing code base in C++ and Python.It is evident that by using current commercial existing mobile platforms such as KUKA (http://www.kukarobotics.com/en/products/mobility/KMR_iiwa/), MIR (http://mobile-industrial-robots.com/en/multimedia-2/videos/) and Universal Robots (https://www.universal-robots.com/), a similar, more sturdy industry-level system can be constructed.
Watch the whole demonstration here: