SemProM

The project Semantic Product Memory (SemProM) focuses on the development

of a flexible mobile dual-arm robot, which adapts its behaviour to the

specificities of the targeted product after extracting information from

its digital product memory. The quality of logistic processes depends on

the knowledge about the specific handling of a product during its

transport. Currently, this information is not continuously available. In

extreme cases this causes product damages in cases of complex transport

processes with changing contract partners. The digital product memory

can be used to store the necessary handling information for the

transport of the product in machine-readable form. The handling

information is thus directly connected to the product and is available

at any time. This yields a high benefit for robot systems that

manipulate products of varying shape. The gripper of the robot

automatically adapts to the specifications provided by the digital

product memory regarding size, weight, lifting points, etc. of the

considered product. It also guarantees that constraints like maximum

acceleration or geometric orientation are not violated.

In

flexible and automatic manufacturing processes, different, non-uniform

items are produced. The high amount of varying products demands for

improved approaches of quality control. Classical control methods are

insufficient here, since they are based on the detection of anomalies of

the considered product compared to a given sample item. The digital

product memory facilitates the storage of relevant parameters during the

whole production process, allowing for conclusions regarding the

quality of the considered product. If it is of inferior quality, the

product can be removed from the production process at an early stage,

improving the efficiency of the overall production process.

The

mobile dual-arm robot is the first system comprising flexible grippers

combined with optimally placed RFID antennae. The digital product memory

is read by the antenna and determines the optimal configuration of the

grippers. In order to establish full Cartesian positioning together with

the ability to cope with obstacles, every arm of the robot has seven

degrees of freedom. The robot will automatically navigate and localize

itself in a factory production hall and automatically approach a

recharging station when its battery power drops below a critical value.

It will also be able to communicate with other components and users in

the environment. Additionally, it is intended to store a summary of the

actual product manipulations performed by the robot in the digital

product memory.