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 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.

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.