Strain wave gears are frequently used in small and medium size industrial robots. In order to describe and quantify friction effects in gearboxes of such type, a structurally simple, yet powerful model is proposed taking into account both speed-and load-dependent friction effects. Moreover, position-dependent disturbances in a robotic joint are considered. An identification procedure is presented that allows to separate the individual components of the model and identify them subsequently. The effectiveness of the model and identification procedure is validated using experimental data gathered from four different robotic joints of varying size. Furthermore, the benefits of improved friction modeling are shown by means of different applications, including smooth lead-through programming and sensorless 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.

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.

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.

A new Robotic Assembly Skill (RAS) modeling framework is proposed. An assembly skill is a primitive that encapsulates the capabilities to coordinate, control and supervise an elementary robot task. To gain reusability of a primitive in alike robot tasks, the primitives are represented as generic templates that are parametrized for each situation with data from an assembly specification. A skill is represented in two ways, namely as a trajectory describing compliant motions in pose-wrench space and as a finite state machine. This approach comes with the potential to simplify robot programming and to improve robustness in robotic assembly due to inherent quality checking. The approach is implemented on an ABB YuMi robot performing the assembly of a programmable logic controller (PLC) I/O module.

Modeling and predicting human behavior is indispensable when industrial robots interacting with human operators are to be manipulated safely and efficiently. One challenge is that human operators tend to follow different motion patterns, depending on their intention and the structure of the environment. This precludes the use of classical estimation techniques based on kinematic or dynamic models, especially for the purpose of long-term prediction. In this paper, we propose a method based on Hidden Markov Models to predict the region of the workspace that is possibly occupied by the human within a prediction horizon. In contrast to predictions in the form of single points such as most likely human positions as obtained from previous approaches, the regions obtained here may serve as safety constraints when the robot motion is planned or optimized. This way one avoids collisions with a probability not less than a predefined threshold. The practicability of our method is demonstrated by successfully and accurately predicting the motion of a human arm in two scenarios involving multiple motion patterns.

Human Robot Interaction (HRI) is an active field of integrating and embedding different techniques in artificial intelligence. This paper describes my research topic on: Control of Robotic Systems for Safe Interaction with Human Operators. It consists of online motion generation for robotic manipulators interactingwith dynamic obstacles and humans using a moving horizon scheme, modeling and long term prediction of human motion using probabilistic models and reachability analysis, and development of an HRI demonstration platform.

This paper considers the task of path planning for articulated robots such that the end effector is driven optimally between two points in the workspace while collision with dynamic obstacles is avoided. Compared to path planning in the configuration space, approaches in the workspace save the computationally expensive step of mapping obstacles from the workspace into the configuration space. The method presented here builds on a problem formulation as a mixed-integer program considering time-varying constraints resulting from moving obstacles, as well as state and input constraints depending on the region of the work space. The method is applied to a two-link robot with static and moving obstacles and is evaluated for different situations.