Björn Matthias

Robot concept for scalable, flexible assembly automation: A technology study on a harmless dual-armed robot

This paper introduces a new robot concept that aims at closing the gap between a manual assembly and a fully automatic assembly. It is intended to be used for handling and assembly of small parts in a highly agile production scenario, which employs both human workers and robots in the same line, with a frequent need for reconfiguration. The development is at a stage where several prototypes leave the research lab and are being tested in pilot applications, with more work required to reach a fully agile assembly scenario. Substantial parts of the remaining research work are done in the FP7 project ROSETTA.

Safety in Human-Robot Collaborative Manufacturing Environments: Metrics and Control

New paradigms in industrial robotics no longer require physical separation between robotic manipulators and humans. Moreover, in order to optimize production, humans and robots are expected to collaborate to some extent. In this scenario, involving a shared environment between humans and robots, common motion generation algorithms might turn out to be inadequate for this purpose. This paper proposes a kinematic control strategy which enforces safety, while maintaining the maximum level of productivity of the robot. The resulting motion of the (possibly redundant) robot is obtained as an output of an optimization-based real-time algorithm in which safety is regarded as a hard constraint to be satisfied. The methodology is experimentally validated on a dual-arm concept robot with 7-DOF per arm performing a manipulation task.

Motor-Current-Based Estimation of Cartesian Contact Forces and Torques for Robotic Manipulators and Its Application to Force Control

We present a Kalman filter-based approach for estimating external forces and torques relying on a dynamic model of a serial-chain robotic manipulator where only motor signals (currents, joint angles, and joint speeds) are measurable. The method does not require any additional sensing compared to standard robot control systems. The approach exploits redundancy in 7DOF arms, but also applies to traditional 6DOF manipulators. Automatic filter calibration routines are presented minimizing the number of parameters that must be tuned in order to successfully apply the proposed scheme and to optimize estimation quality. The approach is verified by measurement data gathered from an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm. Furthermore, measurement results are presented employing force and torque estimates in a compliance control scheme, verifying that the estimation quality achieved is improved compared to existing approaches and is sufficient to employ the estimates in force-controlled applications.Note to Practitioners—More and more robotic applications involve contact with at least partially unknown environments. As a consequence, they require control approaches that go beyond the traditional position control. In particular, information about contact forces and torques has to be taken into account. However, integrating additional sensing equipment to obtain the required force/torque information is often technically challenging and expensive. Cartesian contact force and torque estimation allows obtaining force/torque information solely from available sensors. The estimation technique can be regarded as a virtual sensor, and hence this brief deals with a key technology enabling force controlled robotic applications such as assembly, grinding, and deburring without the need for expensive additional sensing.

Improving contact force estimation accuracy by optimal redundancy resolution

Estimating Cartesian contact forces and torques enables external force supervision for robotic manipulators and even force-controlled applications while avoiding the need for additional external sensing. Redundant manipulators facilitate the problem of Cartesian contact force and torque estimation (CCFE) at the TCP, since an increased amount of joint level information is available for estimating the six components of external wrench. In this paper, we point out that the errors in CCFE estimates arising from inevitable uncertainties in available joint level information (either measurements or estimates) depend on the manipulator configuration. Based on this, an algorithm is proposed that resolves redundancy in an optimal way with respect of the achievable CCFE quality. For a given TCP pose, the joint configuration resulting in minimized CCFE errors is calculated. The proposed method is verified by means of experimental data gathered from a 7DOF manipulator.