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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 (HRC) provides the opportunity to enhance the physical abilities of severely and multiply disabled people thus allowing them to work in industrial workplaces on the primary labour market. In order to assist this target group optimally, the collaborative robot has to support them based on their individual capabilities. Therefore, the knowledge about the amount of required assistance is a central aspect for the design and programming of HRC workplaces. The paper introduces a new method that bases the task allocation on the individual capabilities of a person. The method obtains human capabilities on the one hand and the process requirements on the other. In the following step, these two profiles are compared and the workload of the human is acquired. This determines the amount of support or assistance, which should be provided by a robot capable of HRC. In the end, the profile comparison of an anonymized participant and the concept of the human–robot workplace is presented.

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