As robots and humans increasingly share the same workspace, the development of safe motion plans becomes paramount. For real-world applications, nonetheless, it is critical that safety solutions are achieved without compromising performance. The computation of safe, time-efficient trajectories, however, usually requires rather complex often decoupled planning and optimization methods which degrades the nominal performance. In this work, instead, we cast the problem as a graph search-based scheme that enables us to solve the problem efficiently. The graph search is guided by an informed cost balance criterion. In this context we present the S* algorithm which minimizes the total planning time by equilibrising shortest time-efficient paths and paths with higher safe velocities. The approach is compatible with standards and validated both in rigorous simulation trials on a 6 DoF UR5 robot as well as real world experiments on a Franka Emika 7 DoF research robot.

Flexible and versatile mobile robotic coworkers are becoming an indispensable commodity for helping humans with repetitive or physically demanding work. A key challenge with these systems is respecting the strict safety requirements in shared and collaborative workspaces. This inevitably requires solving their whole-body dynamics to obtain the necessary inertial impact properties. In this article, we present an integrated impact dynamics and safe precollision control framework to address the discussed challenge. We propose a novel modular dynamics approach that provides efficient formulations for reusing the uncoupled subsystem dynamics when evaluating the coupled system. Our approach is generalized for deriving the whole-body impact dynamics of any articulated floating-base robot. Furthermore, it outperforms classical monolithic approaches for computing the dynamics, making it favorable for systems with more than two dynamic subsystems while allowing decentralized computations. Finally, based on the proposed modular and generalized impact dynamics and extending our previous work, we introduce the generalized safe motion unit as a unified safety scheme for floating-base robotic structures with branched manipulation extremities. The proposed concepts are evaluated on an exemplary wheeled mobile manipulator, considering realistic use cases in simulation and real-world experiments. The obtained results validated the efficacy of our framework and developed methods.

Knowledge of the robot's load inertial parameters is indispensable for accurate and safe operation, especially in collaborative robotics. However, an intuitive method for online inertial payload identification, usable while the robot is executing another online generated task, is still lacking. In this work, we propose an online payload identification approach based on the momentum observer using proprioceptive sensors of tactile robots and a novel filter design of kinematic measure-ments. Furthermore, we introduce a novel calibration scheme, that allows circumventing constraints of current calibration methods for payload identification. Specifically, the requirement of performing exactly the same motion for calibration as well as for the identification process is released. This is achieved by introducing an average virtual calibration object that improves the robot model for the identification process. In experiments with a Franka Emika Panda robot, it is shown that the proposed methods surpass common methods in terms of identification error. Especially, the novel calibration approach shows high robustness against temporal and spatial misalignment of the motions.

In physical human-robot interaction (pHRI), safety is a key requirement. As collisions between humans and robots can generally not be avoided, it must be ensured that the human is not harmed. The robot reflected mass, the contact geometry, and the relative velocity between human and robot are the parameters that have the most significant influence on human injury severity during a collision. The reflected mass depends on the robot configuration and can be optimized especially in kinematically redundant robots. In this paper, we propose the Mean Reflected Mass (MRM) metric. The MRM is independent of the direction of contact/motion and enables assessing and optimizing the robot posture w.r.t. safety. In contrast to existing metrics, it is physically interpretable, meaning that it can be related to biomechanical injury data for realistic and model-independent safety analysis. For the Franka Emika Panda, we demonstrate in simulation that an optimization of the robot's MRM reduces the mean collision force. Finally, the relevance of the MRM for real pHRI applications is confirmed through a collision experiment.

Kinesthetic teaching of collaborative robots is applied for intuitive and flexible robot programming by demonstration. This enables non-experts to program such robots on the task-level. Multiple strategies exist to teach velocity- or torque-controlled robots and, thus, the maneuverability among commercial robots differs significantly. However, currently there exists no metric that quantifies how “well” the robot can be guided, e.g., how much effort is required to initiate a motion. In this paper, we propose standardized procedures to quantitatively assess robot manual maneuverability. First, we identify different motion phases during kinesthetic teaching. For each phase, we then propose metrics and experimental setups to evaluate them. The experimental protocols are applied to the proprietary teaching schemes of five commercial robots, namely the KUKA LWR iiwa 14, Yuanda Yu+, Franka Emika robot, and Universal Robot's UR5e and UR10e. The experimental comparison highlights distinct differences between the robots and shows that the proposed methods are a meaningful contribution to the performance and ergonomics assessment of collaborative robots.

In physical human-robot interaction (pHRI), robots need to detect and react to intended and unintended contacts in a safe manner. Proprioceptive sensing capabilities and collision detection and identification techniques differ among commercially available robots, which means that also their sensitivity to detect dynamic collisions with the environment or the human co-worker differ. Up to now, there exists no standardized procedure for assessing the contact sensitivity of a robotic system. In this paper, we propose the concept of contact sensitivity maps (CSM), a relationship between the robot's dynamic impact properties and the reliability of its collision handling. The CSM allows the robot user to determine for which robot workspace areas and dynamic collision parameters (mass, velocity) reliable contact detection and reaction can be expected. We propose a standardized benchmarking procedure and test setup for deriving CSMs. Finally, we analyze and compare the experimental results of the Universal Robots UR10e, UR5e, and Franka Emika Panda, where we observe significant differences in contact sensitivity.

Improving robot systems via newly-developed sensing devices, control algorithms, or state estimators in order to obtain safe and efficient human-robot interaction as well as tactile manipulation skills requires standardized performance measurement protocols for objective comparison. Common protocols to evaluate robot motion performance are currently defined in EN ISO 9283:1998. For tactile and safety performance, however, no common metrics were agreed on nor standardized yet. In this paper, we propose a set of quantifiable performance criteria for robot performance analysis, objectifying robot force sensing, force control, and collision detection/reaction performance. We introduce the corresponding measurement setups and protocols, demonstrate and experimentally validate each with a Universal Robot UR10e and UR5e as well as a Franka Emika Panda robot arm. The proposed performance criteria, metrics, and experimental setups constitute the basis of a fully tactile performance and safety benchmarking framework that allows to objectively evaluate tactile robot performance via reproducible reference tests.

Interactions between robots and their environment give rise to external wrenches acting on the robot structure. The estimation of the resulting torques in the joints is fundamental in human-robot interaction to detect/identify collisions and perform suitable reaction strategies. Other applications may require to use the estimation for compensating the effects of the external torques within the control loop. The well-established momentum observer, which relies on proprioceptive sensors only, is usually used for these purposes. In this work, the momentum dynamics is used to derive new observers. While the classic momentum observer provides a first-order filtered version of the external torques, here a (theoretically) finite-time convergence is achieved. Simulations and experiments are used to validate the performance of the proposed methods.

Kinematic redundancy enhances the dexterity and flexibility of robot manipulators. By exploiting the redundant degrees of freedom, auxiliary null space tasks can be carried out in addition to the primary task. Such auxiliary tasks are often formulated in terms of a performance or safety criterion that shall be minimized. If the optimization criterion, however, is defined in global terms, then it is directly affected by the primary task. As a consequence, the task achievement of the auxiliary task may be unnecessarily detrimented by the main task. In addition to modifying the primary task via constraint relaxation, a possible solution for improving the performance of the auxiliary task is to relax the primary task temporarily via time scaling. This gives the null space task more time for achieving its objective. In this paper, we propose several such time scaling schemes and verify their performance for a DLR/KUKA Lightweight Robot with one redundant degree of freedom. Finally, we extend the concept to multiple prioritized tasks and provide a simulation example.

In practical robotic applications various types of tools are attached for manipulating objects. Besides adding gravitational load to the robot, which results in larger joint torques, such payloads influence the collision safety characteristics through changing surface curvature properties, reflected mass and effective robot speed along a desired motion direction. In this paper, we evaluate the effect a known, unactuated pay-load that is attached to the end-effector has on the intantaneous reflected inertial parameters and maximum task velocity of a robot. The proposed mass update approach relies on the analysis of the kinetic energy matrices, while the velocity maximization is tackled by formulating static optimization problems with different constraints on angular motion of the end-effector. Finally, for analyzing the validity of the introduced approach in the framework of Safety Maps, we discuss simulation results of a PUMA 560 robot that has an exemplary payload attached to its end-effector.

Kinematic redundancy is a characteristic and beneficial property in collaborative robots nowadays as it enhances the flexibility and dexterity of the system. While the robot is manipulating an object, it is often necessary to kinematically reconfigure the robot, for example, when it obstructs the human. For this, internal or so-called null space motions can be carried out which do not affect the main task. In general, it is desirable that the human coworker can anticipate how the robot will move at any time. However, for null space motions this is typically not the case as they are non-intuitive and not suitable for interaction. In this work, we develop intuitive null space interaction behaviors for redundant manipulators, where the human can easily guide the robot. We want to provide users with a tool, that is straightforward to implement and solves real-world problems effectively. Two practical applications for an eight- and ten-DOF robot demonstrate the performance of the proposed method.

Ensuring safety is a primary goal in physical human-robot interaction. In various collision experiments it was found that the robot's effective mass, velocity, and geometry are the key parameters which influence the human injury severity during an impact. Recently, a velocity controller was proposed that limits the robot speed to a biomechanically safe value, taking into account the mass and the curvature in the direction of movement for a given point of interest. The mass and the geometry depend on the mechanical design, however, the effective mass also depends on the robot configuration. In this paper, we exploit the redundant degree(s) of freedom of a joint torque controlled seven- and eight-DOF robot to minimize the effective mass without affecting the desired Cartesian end-effector trajectory and with the goal to improve the performance of the safe velocity controller at the same time. Given recent results in robotics injury analysis, we analyze when such a redundancy resolution scheme actually improves safety. For the considered robots, we find reflected mass extrema that can be obtained by null space motions, and propose a real-time, torque-based redundancy resolution scheme, which is finally verified in experiments.

It has recently been shown that intrinsically elastic robots are capable of outperforming rigid robots in terms of peak velocity by making systematic use of energy storage and release. Certainly, high link side velocities are beneficial for performance, however, they also increase the probability of self damage or human injury in case of a collision. To ensure the physical integrity of both human and robot, it is therefore crucial to avoid potentially dangerous collisions and react in a compliant manner if unwanted contact has occurred or may occur unforeseeable. In this paper, we consider the most intuitive collision anticipation and pre-reaction scheme, namely stopping an elastic robot, if possible in minimum time. For 1-DOF elastic joints with limited elastic deflection we extend existing model-based and model-free controllers and compare their performance. Furthermore, we analyze the braking trajectory that is achieved with the different strategies. The 1-DOF solution is extended to the double pendulum case, where we show that feasible estimates for maximum and final position can be obtained at the very first instant of braking.

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.

Recently, intrinsically elastic joints became increasingly popular due to several reasons. Most importantly, elasticity improves impact robustness and, if used wisely, energy efficiency. Potential energy storage and release capabilities in the joints allow to outperform rigid manipulators by means of achievable peak link velocity. It has therefore been of great interest to find explosive or cyclic motions, similar to those of humans or animals, that make systematic use of joint elasticity. In this context, we address two important control problems in the present paper. First, we find all potential system states that a visco-elastic joint with constrained deflection may reach from its equilibrium state and analyze the influence of system parameters on the according reachable set. While high link velocities are certainly desirable in terms of performance, they may also increase the robot's level of dangerousness and/or the risk of self damage during potentially unforeseen collisions. Thus, we tackle the problem of how to brake a visco-elastic joint in minimum time. Furthermore, the results are extended to a near-optimal real-time control law for elastic n-DOF manipulators. The proposed braking controller is experimentally verified on a KUKA/DLR LWR4 in joint impedance control.

Designing intrinsically elastic robot systems, making systematic use of their properties in terms of impact decoupling, and exploiting temporary energy storage and release during excitative motions is becoming an important topic in nowadays robot design and control. In this paper we treat two distinct questions that are of primary interest in this context. First, we elaborate an accurate estimation of the maximum contact force during simplified human/obstacle-robot collisions and how the relation between reflected joint stiffness, link inertia, human/obstacle stiffness, and human/obstacle inertia affect it. Overall, our analysis provides a safety oriented methodology for designing intrinsically elastic joints and clearly defines how its basic mechanical properties influence the overall collision behavior. This can be used for designing safer and more robust robots. Secondly, we provide a closed form solution of reaching maximum link side velocity in minimum time with an intrinsically elastic joint, while keeping the maximum deflection constraint. This gives an analytical tool for determining suitable stiffness and maximum deflection values in order to be able to execute desired optimal excitation trajectories for explosive motions.

Intrinsically elastic joints have become increasingly popular over the last years. Commonly, they are considered to outperform rigid actuation in terms of peak dynamics, robustness, and energy efficiency. In particular, the possible increase of link speed by adequate motor excitation trajectories, such that the elastic transmission temporarily stores elastic energy and then timely converts it into kinetic link energy, is a new control problem in robotics. However, despite being a popular argument in favor of elastic actuation, it was not shown yet that this potential speed gain is truly inherent to the physical properties of the mechanism. In order to argue that “elasticity is superior to input torque”, i.e. size and weight, it still needs to be derived that this new feature does not come at the cost of increasing weight for a given actuation technology. Therefore, we analyze, under which circumstances “extracting” a certain amount of mass from a rigid joint and “investing” this into an elastic mechanism in the drive train leads to such a performance increase. For this, we derive the general scaling behavior of rigid joints and compare their capabilities in terms of maximum velocity to the performance behavior of an elastic joint, while taking into consideration the most important real-world constraints.