The objective of this letter is to propose a drive-based control method for damping joint vibrations in order to improve the machining quality and the productivity of robot machining. The machining quality is significantly determined by the transfer behavior of the gearboxes installed in the robot joints. This letter presents a novel approach for drive-based damping of these vibrations and thus for increasing the dynamic path accuracy on the basis of secondary encoders. The approach represents an enhancement of the independent joint control, which is widely used in industry. The simulation as well as the experimental results on a KUKA KR210-2 demonstrate the effectiveness of the presented control method for damping gearbox vibrations and increasing the dynamic path accuracy of industrial robot manipulators.

Automating complex tasks using robotic systems requires skills for planning, control and execution. This paper proposes a complete robotic system for maintenance automation, which can automate disassembly and assembly operations under environmental uncertainties (e.g. deviations between prior plan information). The cognition of the robotic system is based on a planning approach (using CAD and RGBD data) and includes a method to interpret a symbolic plan and transform it to a set of executable robot instructions. The complete system is experimentally evaluated using real-world applications. This work shows the first step to transfer these theoretical results into a practical robotic solution.

The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this paper, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which uses a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning, we use global state-of-the-art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed.

Software-Defined Manufacturing (SDM) is a new paradigm for how to manufacture in the Industry 4.0 factory of the future. The approach requires a fundamental change in thinking and reinvention of central business models in production technology. This paper discusses the main challenges on the way to a software-defined factory of the future and illustrates possible solutions. An exemplary future factory is presented, in which the validation of the solutions is carried out. For this purpose, cloud-based Computerized Numerical Control (CNC) is considered as use case. Experimental results demonstrate the proof of concept and show the potential for further applications.

Large industrial robots are inexpensive compared to their workspace. Due to flexibility, the positioning accuracy is compromised. This paper presents a simple yet effective static calibration method for robots using a laser tracker that can be applied to processes with no change in external load and without high processing forces. The calibration method is based on a novel elastokinematic model that captures the nonlinear gearbox and link compliance and also accounts for the effects of pose dependent joint friction and nonlinearities due to the gravity compensation mechanism. Furthermore, a feedforward concept is presented that separates kinematics from compliance to reduce computation time. The calibration method was implemented on industrial hardware and experimentally validated on a KUKA KR210–2. In comparison to state of the art compliance modeling, accuracy is improved by 23.1 %. Finally, the feedforward model reduces the mean positioning error in the entire workspace by 54.9% compared to the purely kinematically calibrated model, emphasizing the need for compliance compensation.