Intelligent work ergonomics using sensory exoskeletons and autonomous transport systems for enhanced human-technology interaction in automotive cargo handling
The cargo handling environment in ports is characterized by the handling of heavy and large loads, in which humans are essential despite the progress of automation. In the specific application of automobile handling, the vehicles are prepared for the respective target market in technical centers. For this purpose, tires and trailer couplings, for example, have to be moved and mounted by humans. In addition, there is a large number of additional car parts that have to be picked and, in some cases, assembled in an overhead position. As a result, a high physical strain is placed on the employees, which leads with increasing age to a degree of physical impairment. Within the scope of the project MEXOT, the challenges identified are addressed with a socio-technical development approach. To this end, the use of exoskeletons is targeted, aiming to research on intelligent work ergonomics, which examines human-machine interaction in combination with exoskeletons and automated guided vehicles (AGVs). Motion sensors will be integrated into a passive exoskeleton to track the movement patterns of the employees. First, this information is used to enrich data for an external incentive system that rewards employees for wearing the exoskeleton correctly and integrates gamification approaches to increase motivation. In a second step, the data and process information are used to activate or deactivate individual "elastomeric muscles", aiming at a higher wearing flexibility for activities that do not require physical support. In the third step, the movement information of the exoskeleton will be used to develop a sophisticated pick- and assembly-by-motion concept, which, in combination with the camera system of the AGV, enables the registration of individual work steps in picking and assembly. For the AGV, further research is conducted on increasing productivity and reducing the workload of employees through process-specific and worker-individualized material supply. Moreover, voice- and gesture-based functionalities are implemented for human-machine interaction with the AGV.
01.01.2022 - 31.12.2024,
Funded by BMVI
- C. Petzoldt () (Project manager)
ROS-based Education of Advanced Motion Planning and Control
This project aims at reducing technological barriers towards using a fleet of robots in warehouses and conventional manufacturing environments. This project creates learning material to upskill university students and professionals in advanced autonomous navigation concepts, specifically how to leverage existing open-source software libraries on mobile robot platforms. From end-user perspective, our education materials will help industries using mobile robot solutions to perform complex debugging/maintenance without overly relying on their third-party supplier. This will save time spent tuning motion planning libraries without being fully aware of the effect of underlying hyperparameters.
Duration 01.01.2022 - 31.12.2022, Funded by EU - EIT Manufacturing
- T. Sprodowski () (Project manager)
Palletized Loads Automatic Loading System for unmodified European Trailers to enable a Resilient Supply Chain
The manufacturing facilities in Europe are mostly fully automated with minimum touch on pallets from production all the way up to the docks but the last mile of action, i.e. loading operation remains fully manual with no flexibility to decide on how to execute this task (automated or manual). This makes it a weak link in the supply chain, which is prone to disruption (especially as learnt in COVID pandemic situation) as it is fully dependent on human presence to execute a labor intensive and less ergonomic task. Hence true supply chain resilience cannot be achieved until there is a solution developed to automatically load palletized goods with on the road (un-modified) European trailers.
The main reason why this task is still conducted manually is the non-standard trailer fleet in Europe and the lack of no automatic solution available for curtain trailers. Given that curtain trailers comprise at least 80% of on the road trailers there is a huge opportunity with high scalability for a solution.
However, currently existing solutions for automatic loading of pallets only work for loading rigid-walled trucks, which are characterized by rigid, nondeformed walls. In contrast, for loading of curtain trailers, such systems fail due to the varying conditions of curtain trailers and less defined walls resulting in these systems to crash into obstacles like carrier beams causing damaged loads or resulting in emergency stops. Consequently, this activity aims to enable an existing automatic loading solution (Nalon) of the company Duro Felguera to tackle the challenges associated with automatically loading curtain trailers from the rear side.
Duration 01.01.2022 - 31.12.2022, Funded by EU - EIT Manufacturing
- L. Rolfs () (Project manager)
- D. Niermann ()
Intelligent end effector component protection for safe human-robot collaboration and coexistence
The aim of the project is to develop an intelligent modular end effector-component protection system for safe and intuitive cooperation between humans and robots. The protection system is to consist of a protective cocoon that is installed on the end effector and encloses both it and the component. In this way, people in the direct cooperative working area of the robot can be protected from hazards. Integrated sensors will detect people in the vicinity at an early stage and automatically keep them at a distance. With the help of intelligent control strategies, robot movements should be possible both spontaneously (avoidance) and anticipatory, so as not to interrupt processes unnecessarily. Interaction modules are to intuitively indicate the robot's next movement so that the movement can be anticipated and also serve as an interface for the input of commands. Thus, for the first time, humans can also react intuitively to the robot, which increases work safety and minimizes safety-related interruptions.
Duration 01.10.2021 - 30.09.2023, Funded by BMWi
- A. Heuermann () (Project manager)
Smart Learning in Logistics
In the project, a vocational education and training platform for employees in logistics is to be developed on the basis of an existing platform (MARIDAL), which enables demand-oriented and individual learning and offers flexible learning paths. The platform envisaged in this project is a digital learning ecosystem. AI is used to personalise the user experience and support learning. In addition, certificates can be issued - also for external persons.
Duration 01.09.2021 - 31.08.2024, Funded by BMBF
- H. Duin ()