People with disabilities (PwD) have the right to equal participation in the labor market under the 2008 Convention on the Rights of Persons with Disabilities [1], yet OECD data indicates that PwD remain excluded, with a 15% unemployment rate in 2019 across 32 OECD countries, and an 8.6% higher unemployment rate compared to people without disabilities [2]. With the growth of the elderly population and, consequently PwD, employers will face labor shortages [3]. The European Commission advocates for technology, like human-robot collaboration, to create ergonomic workplaces adapted to individual needs, aiming to enhance PwD inclusion and alleviate labor shortages [4].

As labor shortage is rising at an alarming rate, it is imperative to enable all people to work, particularly people with disabilities and elderly people. Robots are often used as universal tool to assist people with disabilities. However, for such human-robot workstations universal design fails. We mitigate the challenges of selecting an individualized set of input and output devices by matching devices required by the work process and individual disabilities adhering to the Convention on the Rights of Persons with Disabilities passed by the United Nations. The objective is to facilitate economically viable work-stations with just the required devices, hence, lowering overall cost of corporate inclusion and during redesign of workplaces. Our work focuses on developing an efficient approach to filter input and output devices based on a person's disabilities, resulting in a tailored list of usable devices. The methodology enables an automated assessment of devices compatible with specific disabilities defined in International Classification of Functioning, Disability and Health. In a mock-up, we showcase the synthesis of input and output devices from disabilities, thereby providing a practical tool for selecting devices for individuals with disabilities.

Safety is a central challenge in human-robot collaboration. Particularly in higher collaboration levels, separating safety devices, such as fences, are no longer needed and must be replaced by intelligent sensor-based systems. Of particular interest is the adaptive speed control of the robot. This work presents a methodology to adaptively control the end-effector velocity of the robot based on the distances to dynamic environmental objects. The method combines distance measurement and environmental subtraction with conservative velocity estimation using robot-specific stopping distances and is available in real-time. Data acquisition is performed using a co-moving 3D camera sensor attached to the robot structure.

Human-robot collaboration offers the advantage of combining human characteristics and robotic capabilities, balancing individual weaknesses. In the inclusive project Next Generation, we are exploring the possibility of using collaborative robots as assistive devices for people with severe and multiple disabilities. An important step in implementing an inclusive workstation with a collaborative robot is determining the level of assistance required. Therefore, we developed a novel methodology and a capability-based software tool to determine the individual level of challenge. In this paper, we present the developed tool and its validation. We validate the methodology and tool using an industrial sample application from first labor market incorporating participants with varying mental and physical disabilities.