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For years, Festo has been developing process automation solutions for a broad range of industry sectors. By transferring our experience from one industry to another, we take advantage of valuable synergies and create economies of scale.

This flyer offers insights into the job profile, competences and application fields of water technicians. It showcases why water management is important for today's sustainability activities and how it is integrated into WorldSkills competitions.

“Internet of Things” (IoT) and “Industry 4.0” (I4.0) are buzzwords in the process manufacturing and automation world, with far-reaching benefits understood by most at a high level. But, what relevant and practical data and understanding can I4.0 bring your company in everyday operations, such as monitoring compressed air consumption, process optimization, enabling energy efficiencies, and providing preventative diagnostics?

An artificial bird is introduced which was developed using two new

features in biologically-inspired flight, active torsion and partially linear

kinematics. Active torsion rests on well established theoretical

predictions in unsteady aerodynamics. The concept of partially linear

kinematics is inspired by zoological observations on flying locusts.

When the wings flap upwards, the servomotor for the active torsion

turns the outer wing from a positive angle of incidence within

a short fraction of the flapping period into a negative angle of incidence.

Between the turning points the angle of torsion remains

constant. Numerical calculations confirm the expected benefits

compared to passive torsion.

On a novel pneumatic four-finger gripper with three degrees of freedom per finger we apply reinforcement learning to learn dexter- ous manipulation of objects. In order to reduce the search space, we implemented hierarchical learning on two levels. Low-level learning is used for basic movement primitives like grabbing, lifting or rotation of an object around three cartesian axes, whereas in high-level learning we use the already learned low-level actions to find a policy that ena- bles the gripper to move a target point on the surface of a sphere to the top position in a few seconds. It turns out that Q-learning with a finite state- and action space solves the learning task very well.