Speed and separation monitoring (SSM) as well as power and force limiting (PFL) are two of the four permissible collaborative operations in human-robot interaction (HRI). Current standards and guidelines provide users and system integrators with a simple basis to calculate permissible separation distances between human workers and robots. However, problems occur in practical implementations, as the safety zones are oversized due to various simplifications and corresponding path velocities have to be significantly reduced. This leads, for example, to cycle time losses and wasted space within the respective HRI application. The present paper describes an approach to combine the SSM and PFL collaborative operations and to exploit the optimization potential for robot motion planning and the design of collaborative workstations. To localize the necessary human body regions, we integrate a method from the field of machine learning. Using an exemplary HRI scenario with the PILZ lightweight robot PRBT, we validate the approach presented here and discuss initial results.

The number of industrial applications where a robot needs to unload disordered parts is increasing substantially. The usage of robot vision is highly preferred to obtain reliable results. In the past different algorithms for localizing objects as well as for motion planning to avoid collisions between gripper, object and environment were presented and even some commercial Bin-Picking systems are available. Nevertheless a realistic simulation taking account of various vision and robot systems and path planning strategies to predict cycle times is still not possible. In this paper a universal software framework with the focus on virtual Bin-Picking is presented. Utilizing a generic approach this framework enables the integration of various algorithms for object recognition, motion planning, different types of robots, grippers and vision systems. Thus an efficient simulation of different (virtual) Bin-Picking setups including equipment such as sensor devices or robot controllers within a virtual environment is possible. Furthermore the use of such a system allows the prediction of cycle times, percentage of tangible objects or testing of different Bin-Picking setups.

3D simulation and flexible control of robot-based workcells are in common use. However, many companies, especially SMEs, do not make use of the advantages these technologies imply. Reasons for this behavior are the costs of the software and hardware systems as well as the fact that one needs experts to operate those systems. This article describes an innovative architecture that has been developed for a corporate integration of simulation and control systems into production processes. The coexistence and cooperation of PC-based simulation and control makes the cost-effective use of virtual reality (VR) methods and a wide range of novel industrial applications possible. By use of VR technologies complex systems can be operated by any user without long periods of training. Thus, small- and middle-size companies are enabled to use innovative technologies to increase their productivity and competitiveness.