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%.

The use of pointed or edged tools or objects is one of the most challenging aspects of today’s application of physical human-robot interaction (pHRI). One reason for this is that the severity of harm caused by such edged or pointed impactors is less well studied than for blunt impactors. Consequently, the standards specify well-reasoned force and pressure thresholds for blunt impactors and advise avoiding any edges and corners in contacts. Nevertheless, pointed or edged impactor geometries cannot be completely ruled out in real pHRI applications. For example, to allow edged or pointed tools such as screwdrivers near human operators, the knowledge of injury severity needs to be extended so that robot integrators can perform well-reasoned, time-efficient risk assessments. In this paper, we provide the initial datasets on injury prevention for the human hand based on drop tests with surrogates for the human hand, namely pig claws and chicken drumsticks. We then demonstrate the ease and efficiency of robot use using the dataset for contact on two examples. Finally, our experiments provide a set of injuries that may also be expected for human subjects under certain robot mass-velocity constellations in collisions. To extend this work, testing on human samples and a collaborative effort from research institutes worldwide is needed to create a comprehensive human injury avoidance database for any pHRI scenario and thus for safe pHRI applications including edged and pointed geometries.

Integrating humanoid service mobile robots into human environments presents numerous challenges, primarily concerning the safety of interactions between robots and humans. To address these safety concerns, we propose a novel approach that leverages the capabilities of digital twin technology by tailoring it to incorporate comprehensive and robust safety concepts. This paper introduces a "safe-by-design" digital twin that operates alongside the real twin robot in the loop, engaging real-time safety framework during physical interactions with the surrounding environment, including humans.To validate the effectiveness of our proposed safe-by-design digital twin framework, we conducted experiments using a humanoid service mobile robot alongside simulated human counterparts. Our results demonstrate the capability of the integrated impact safety module within the proposed digital twin approach to limit the velocities of both the robot’s base and arms, adhering to injury biomechanics-based safety thresholds. These findings emphasize the promise of our proposed approach for ensuring the physical safety of humanoid service mobile robots operating in dynamic human environments. It enables the digital twin to preemptively identify potential safety hazards and formulate safe intervention actions to ensure the robot’s compliance with safety regulations, paving the way for safer and more widespread adoption of robotic systems in various service domains.

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.

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.