This paper addresses motion replanning in human-robot collaborative scenarios, with an emphasis on reactivity and safety-compliant efficiency. While existing human-aware motion planners perform well in structured environments, they often struggle with unpredictable human behavior. This can result in safety measures that hinder the robot’s performance and overall throughput. This study combines reactive path replanning and a safety-aware cost function, enabling the robot to adapt its path to the changes in the scene in real-time. This solution reduces the execution time and trajectory slowdowns while ensuring safety. Simulations and real-world experiments show the method’s effectiveness compared to standard human-robot cooperation approaches, with efficiency enhancements of up to 60%.

Compliance with global guidelines for sustainable and responsible production in modern industry requires a comparative analysis of consumer devices' energy consumption (EC). This also holds true for the newly established generation of lightweight industrial robots (LIRs). To identify potential strategies for energy optimization, standardized benchmarking procedures are required. However, to the best of the authors' knowledge, there is currently no standardized method for benchmarking the EC of manipulators. In response to this need, we have developed a comprehensive benchmarking framework to evaluate the EC of various LIR designs, delving into the theoretical power consumption under both static and dynamic conditions. Our analysis has led to the proposal of seven proposed metrics—three static and four dynamic. The static metrics—controller consumption, joint electronics consumption, and mechanical brakes' consumption—evaluate the maintenance EC of the robot. Meanwhile, we suggest three dynamic metrics that gauge the system's energy efficiency during motion, with or without payload. We extend this metrics selection by introducing the cost of transportation map for manipulators. For each of the metrics, we suggest a standardized measurement procedure based on state-of-the-art norms and literature. The metric set and experimental procedures are demonstrated using five manipulators (UR3e, UR5e, FR3, M0609, Gen3). Among the results, we can see interesting trends for future optimization of the electronic components and their architecture, e.g., reducing the robot's EC by decentralizing computation via low-consumption onboard controllers for basic tasks and external servers for complex ones.

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.

Strategies for safe human-robot interaction (HRI), such as the well-established Safe Motion Unit, provide a velocity scaling for biomechanically safe robot motion. In addition, psychologically-based safety approaches are required for trustworthy HRI. Such schemes can be very conservative and robot motion complying with such safety approaches should be time efficient within the robot motion planning. In this study, we improve the efficiency of a previously introduced approach for psychologically-based safety in HRI via a Model Predictive Control robot motion planner that simultaneously adjusts Cartesian path and speed to minimise the distance to the target pose as fast as possible. A subordinate real-time motion generator ensures human physical safety by integrating the Safe Motion Unit. Our motion planner is validated by two experiments. The simultaneous adjustment of path and velocity accomplishes highly time efficient robot motion, while considering the human physical and psychological safety. Compared to direct path velocity scaling approaches our planner enables 28 % faster motion execution.

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.

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

In modern highly dynamic robot manipulation, collisions between a robot and objects may be intentionally executed to improve performance. To distinguish between these deliberate contacts and accidental collisions beyond the limit of state-of-the-art human-robot interactions, new sensing approaches are required. This work seeks an easy-to-implement and real-time capable solution to detect the identity of the impacted material. We developed an inertial measurement unit (IMU) based setup that records vibration signals occurring after collisions. Furthermore, a data-set was generated in an unsupervised learning manner using the measurements of collision experiments with several materials commonly used in realistic applications. The data-set was used to train an artificial neural network to classify the type of material involved. Our results show that the neural net detects collisions and a detailed distinction between materials is achieved, even with estimating different human body parts. The unsupervised data-set generation allows for a simple integration of new classes, which provides broader applicability of our approach. As the calculations are running faster than the control cycle of the robot, the output of our classifier can be used in real-time to decide about the robots reaction behavior.

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.

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.

Adaptive cable-driven parallel robots can adjust the position of one or more pulley blocks to optimize performance within a given workspace. Because of their augmented kinematic redundancy, adaptive systems have several advantages over their traditional counterparts featuring the same numbers of cables. In this paper, we explore the application of adaptive cable-driven robots to cable-suspended camera systems. Performance of the traditional and of the adaptive designs are analyzed, using dexterity and stiffness as performance metrics. Results show superior performance of the adaptive design compared to the traditional system. An illustrative design problem for adaptive cable-suspended camera systems is also presented and solved.

Forward fall is one of the most common causes of upper extremity fractures. Significant factors influencing impact force and injuries were widely studied; however, it is necessary to investigate the natural reactions of humans during a forward fall to obtain a realistic evaluation of injuries. The purpose of this study was to analyze the natural motion of the upper extremity during an induced trip. We carried out a tripping experiment using an obstacle colliding with one leg; while recovery step was prevented to produce a forward fall. Results showed that the elbow extension had a slight ascending trend during the forward fall and elbow angle at the moment of hand-ground contact was appropriate to reduce the peak force. Landing on the obstacle-side hand was more likely due to body rotation towards the obstacle-side. To prevent injuries, subjects were connected to a safety harness not to strike the ground with high impact velocity. Thus, the fall motion was simulated using a 12 DOF model to obtain a realistic evaluation of the impact velocity and the related impact force caused by the forward fall was estimated using a sagittal 3-segment model. Results of this study can be useful in human-robot collaboration, where a collision between human and robot may cause a forward fall.