In the modern manufacturing process, novel technologies enable the collaboration between humans and robots, which increases productivity while keeping flexibility. However, these technologies also lead to new challenges, e.g., maximization of Human-Robot Collaboration (HRC) performance while ensuring safety for the human being in fenceless robot applications. In this paper, an approach of the dynamic human motion projection is proposed for typical assembly tasks. The human upper body is simplified as a five-degree-of-freedom (5DOF) rigid-body model. A control-oriented projection model is proposed, and its parameters are estimated from the test data of human capability. Combined with a human-state estimator and a collision estimator, the "worst-case" collision motion is projected in the HRC scenario. The dynamic projection method is feasible online. Finally, the estimated collision time is adopted to increase the robot’s speed limit, which validates the improvement of HRC’s efficiency.

A significant barrier regarding a successful implementation of fenceless robot cells into manufacturing areas with humans is given by the inefficiency due to safety requirements. Robot motions have to be slowed down so that an unexpected collision with a human does not result in human injuries. This velocity reduction leads to longer cycle times and, hence, fenceless robot cells turn out as uneconomic. In this paper, a new approach for human-robot collaboration in assembly tasks is presented. For a better performance of the robot, methods are investigated on how the robot can exploit a maximum performance while maintaining the safety of collaborating humans. For this purpose, the kinematics and dynamics of a human arm are described by a control-oriented dynamic model to determine its capability and reachability. Successful experiments validate the dynamic model as well as a corresponding projection approach for calculating possible movements of the human arm that may lead to a collision with the robot. Finally, this information is used to calculate an admissible path velocity that minimizes the danger of human injuries.

A fundamental problem in human-robot collaboration is to ensure safety for humans being located in the workspace of the robot. Several new robots, referred to as collaborative robots, are pushing into the market. Most of these so-called co-bots have similar properties. They are small, lightweight and designed with big roundings to ensure safety in the case of a collision with a human. Equipped with torque sensors, external torque observers, tactile skins, etc., they are able to stop the robot when an emergency occurs. While developing more and more co-bots, the main focus lies on the robot itself. But to make a robot deployable, a special tool for a defined task is needed. These tools are often sharp-edged and dangerous in case of a collision with a human. In this paper we present a new safety module for robots to ensure safety for different tools in collaborative tasks. This module, filled with air pressure during the robot motion, covers mounted tools and carried workpieces. In case of a non or very slow moving robot, the safety module is able to pull back and the tool is uncovered. In our experiments we found out that we can increase the velocity up to 1 m/s while satisfying the requirements of the ISO/TS 15066 and retain the full functionality of the tool.

Motion planning in robotics is a very large field of research. Many different approaches have been developed to create smooth trajectories for robot movement. For example there are optimization algorithms, which optimize kinematic or dynamic properties of a trajectory. Furthermore, nonlinear programming methods like e.g. optimal control, or polynomial based methods are widely used for trajectory generation. Most of these techniques are used to calculate a trajectory in advance, or they are limited to create point-to-point motions, where the robot needs to stop when switching to the next target point, especially, when interpolating in rotational space. In this paper, we combine a low-pass filter and spherical linear interpolation to realize a velocity-limited online trajectory generator for robot orientations in quaternion space. We use the developed motion generator for mirroring a human arm motion with a robot, recorded by a low frequency visual tracking. Using the proposed method, we can replicate the motion of the operator's arm with very little delay and thereby achieve an easy-to-use interface. Furthermore, as we can strictly limit the velocity of the generated motion, the approach can safely be used in human robot collaboration applications.

Interaction control is a mature, but still, current area of research in robotics. Various approaches have been developed for passive and active force regulation and tracking, e.g. impedance control, direct force and hybrid force-position control. The latter method implements a force feedback outer loop on top of a position controller inner loop, where commands are issued with respect to a compliant frame. However, if the compliant frame undergoes a rigid transformation, e.g. in order to specify the same task relative to another reference frame, then commands can be rendered non-compliant with the task at hand. As a consequence, the robot can be damaged or hurt somebody, this is further aggravated by the rigidness of a position controller. In the present paper, we propose a unified impedance and hybrid force-position control scheme to address such issues. The unified controller benefits from the impedance control compliance while an explicit force value can be achieved. Furthermore, we augment the designed controller with a kinestatic filter, which ensures that the commanded pose and wrench are consistent with a given task model. We validate the designed system through experiments with a lightweight robot (LWR). The proposed approach finds applications in industrial settings where interactions with the environment are required in order to fulfill a task and the system must be robust w.r.t. input commands.

Many future application scenarios of robotics envision robotic agents to be in close physical interaction with humans: On the factory floor, robotic agents shall support their human co-workers with the dull and health threatening parts of their jobs. In their homes, robotic agents shall enable people to stay independent, even if they have disabilities that require physical help in their daily life - a pressing need for our aging societies. A key requirement for such robotic agents is that they are safety-aware, that is, that they know when actions may hurt or threaten humans and actively refrain from performing them. Safe robot control systems are a current research focus in control theory. The control system designs, however, are a bit paranoid: programmers build “software fences” around people, effectively preventing physical interactions. To physically interact in a competent manner robotic agents have to reason about the task context, the human, and her intentions. In this paper, we propose to extend cognition-enabled robot control by introducing humans, physical interaction events, and safe movements as first class objects into the plan language. We show the power of the safety-aware control approach in a real-world scenario with a leading-edge autonomous manipulation platform. Finally, we share our experimental recordings through an online knowledge processing system, and invite the reader to explore the data with queries based on the concepts discussed in this paper.

Elastic robots have a distinct feature that makes them especially interesting to optimal control: their ability to mechanically store and release potential energy. However, solving any kind of optimal control problem for such highly nonlinear dynamics is feasible only numerically, i.e. offline. In turn, optimal solutions would only contribute a clear benefit for dynamic environments/tasks (apart from rather general insights), if they would be accessible/generalizable in real-time. In this paper, we propose a framework for executing near-optimal motions for elastic arms in real-time. We approach the problem as follows. First, we define a set of prototypical optimal control problems. These represent a reasonable set of motions that an intrinsically elastic robot arm is sought to execute. Exemplary, we solve the optimal control problem for some of these prototypes in a roughly covered task space. Then, we encode the resulting optimal trajectories in a dynamical system via Dynamic Movement Primitives (DMPs). Finally, a distance and cost function based metric forms the basis to generalize from the learned parameterizations to a new unsolved optimal control problem in real-time. In short, we intend to overcome the well known problems of optimal control and learning with associated generalization: being offline and being suboptimal, respectively.

Intrinsically elastic robots, which technically implement some key characteristics of the human muskoskeletal system, have become a major research topic in nowadays robotics. These novel devices open up entirely new control approaches. They base on temporary storage of potential energy and its timed transformation into kinetic energy. In legged locomotion, such considerations have been a common tool for unveiling the respective fundamental physical processes. However, in arm control, elasticities were typically considered parasitic. In this video we outline our efforts in exploiting the inherent capabilities of intrinsically elastic robots in order to bring them closer to human performance. Instead of applying purely kinematic learing-by-demonstration approaches, which are certainly suboptimal, we argue for using model based techniques in order to optimally exploit the system dynamics such that highly dynamic motion and manipulation capabilities can be achieved. In particular, the explicit use of elasticities as temporary energy tanks can be fully exploited, if they are modeled adequately as an integral part of the mechanism. We also believe that such approaches can substantially contribute to the understanding of human motion biomechanics.