Close physical human-robot interaction makes it essential to ensure human safety. In particular, the intrinsic safety characteristics of a robot in terms of potential human injury have to be understood well. Then, minimal potential harm can be made a key requirement already at an early stage of the robot design. In this paper, we propose the safety map concept, a map that captures human injury occurrence and robot inherent global or task-dependent safety properties in a unified manner, making it a novel, powerful, and convenient tool to quantitatively analyze the safety performance of a certain robot design. In this paper, we derive the concept and elaborate the map representations of the PUMA 560, KUKA Lightweight Robot IV+, and injury data of the human head and chest. For the latter, we classify and summarize the most relevant impact studies and extend existing literature overviews. Finally, we validate our approach by deriving the safety map for a pick and place task, which allows to assess human safety and guide the task/robot designer how to take measures in order to account for both safety and task performance requirements, respectively.

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.

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.

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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.