This paper presents a novel approach for time-optimal path parameterization based on reachability analysis for robotic systems with viscous friction in the dynamics and jerk constraints. The main step of the method is the backward propagation of controllable sets through a linear second-order system. In order to avoid the unbounded growth of the number of constraints, the sets are approximated by a ray shooting algorithm. Using a convex relaxation, the required set expansion can be solved with second-order cone programming. Evaluation results for a 6-degree of freedom (DOF) robot arm highlight the advantages of the method for computing jerk-limited trajectories.

Dynamic Movement Primitives (DMPs) are a well-known tool for encoding robotic motions. Their popularity stems from invariance properties in time and space, the ability to describe complex coordinated motions in multiple degrees of freedom with a relatively small number of parameters, and the linearity in the parameters that describe the motion. The latter allows easily fitting a DMP to motions e.g. demonstrated by a human. DMPs are at their core second order autonomous differential equations. However, feedforward controls of robots with flexible joints are known to require reference trajectories up to the fourth derivative of position. Consequently, classical DMPs are mechanically not compatible with flexible joint robots. In this paper, we propose an extension of DMPs by introducing FlexDMPs. This concept retains the structural properties and benefits of classical DMPs but generates trajectories up to the fourth derivative that can theoretically be tracked ideally (i.e. with zero tracking error) by flexible joint robots. The concept is demonstrated on a high fidelity simulation model of an industrial robot and in experimental results on a collaborative manipulator.

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.

Robotic assembly tasks are subject to uncertainties arising from part tolerances. A popular approach to deal with such partially unstructured environments is to introduce compliance by using admittance control schemes. For such schemes, contact forces and torques are speed dependent, which often limits the achievable assembly speed. To overcome this limitation, we present a new method for increasing the achievable speed of compliant manipulators by iteratively reducing contact forces. The presented concept of Iteratively Learned and Temporally Scaled Force Control (ILTSFC) is based on two coupled iterative learning controllers where one increases the assembly speed and the other one adjusts the reference trajectory to reduce contact forces. The approach is verified by an experimental peg-in-hole study on an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm.

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.

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.