DYNAMIC AGILE PRODUCTION ROBOTS THAT LEARN AND OPTIMISE KNOWLEDGE AND OPERATIONS
Agile Production crucially depends on the effectivity of intralogistics processes. Robots as components of these processes have the potential to be a game changer provided they are highly flexible, capable, cost- and energy-efficient, safe and able to operate in work environments shared with humans. However, the current state of the art falls short of providing these capabilities given the requirements for future production systems.
Thus, DARKO sets out to realize a new generation of agile production robots that have energy-efficient elastic actuators to execute highly dynamic motions; are able to operate safely within unknown, changing environments; are easy (cost-efficient) to deploy; have predictive planning capabilities to decide for most efficient actions while limiting associated risks; and are aware of humans and their intentions to smoothly and intuitively interact with them.
To maximize its impact, DARKO is aligned with use cases at the largest manufacturer of home appliances in Europe. It will demonstrate, in relevant scenarios, autonomous capabilities significantly beyond the current state of the art in dynamic manipulation (e.g., throwing of goods, picking and placing objects while in motion), perception, mapping, risk management, motion planning and human-robot interaction. Beyond its impact through improved capabilities in these areas, DARKO will provide answers to the questions where and how dynamic manipulation should be integrated as the most efficient solution in intralogistics. Since arm manipulators can, in principle, display super-human performance in terms of accuracy and repeatability, the value of integrating dynamic manipulation, e.g. throwing, into transport processes may well exceed current expectations.
The DARKO consortium is uniquely placed to tackle this ambitious and challenging project. It brings together leading academic and corporate researchers, technology providers and end-users, with the required long-standing expertise.
This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 101017274