Energy-Based Injury Protection Database: Including Shearing Contact Thresholds for Hand and Finger Using Porcine Surrogates

Robin Jeanne Kirschner; Anna Huber; Carina M. Micheler; Dirk Müller; Nader Rajaei; Rainer Burgkart; Sami Haddadin

cs.ROFeb 23, 2026

Energy-Based Injury Protection Database: Including Shearing Contact Thresholds for Hand and Finger Using Porcine Surrogates

Authors: Robin Jeanne Kirschner, Anna Huber, Carina M. Micheler, Dirk Müller, Nader Rajaei, Rainer Burgkart, Sami Haddadin

Organizations: Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, 80992 Munich, Germany · TUM Hospital, Department of Orthopaedics and Sports Orthopaedics, 81675 Munich, Germany · Institute for Machine Tools and Industrial Management, TUM School of Engineering and Design, Technical University of Munich, 85748 Garching near Munich, Germany · Mohamed Bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, United Arab Emirates

Abstract

While robotics research continues to propose strategies for collision avoidance in human-robot interaction, the reality of constrained environments and future humanoid systems makes contact inevitable. To mitigate injury risks, energy-constraining control approaches are commonly used, often relying on safety thresholds derived from blunt impact data in EN ISO 10218-2:2025. However, this dataset does not extend to edged or pointed collisions. Without scalable, clinically grounded datasets covering diverse contact scenarios, safety validation remains limited. Previous studies have laid the groundwork by assessing surrogate-based velocity and mass limits across various geometries, focusing on perpendicular impacts. This study expands those datasets by including shearing contact scenarios in unconstrained collisions, revealing that collision angle significantly affects injury outcomes. Notably, unconstrained shearing contacts result in fewer injuries than perpendicular ones. By reevaluating all prior porcine surrogate data, we establish energy thresholds across geometries and contact types, forming the first energy-based Injury Protection Database. This enables the development of meaningful energy-limiting controllers that ensure safety across a wide range of realistic collision events.

Figures & tables

Fig. 1 : Shearing collisions, common in real-world contact scenarios, are not yet covered by existing energy-based safety thresholds. To investigate injury severity, the robot is modeled as a moving mass with defined impact geometry and collision angle. Surrogate experiments provide initial energy limits based on contact geometry, enabling estimation of safe energy densities for non-perpendicular impacts and adding it to the Injury Protection Database.

Fig. 2 : Schematic of the shearing contact and energy distribution as well as skin deformation. The individual layers of tissue can be seen here qualitatively specifically on the back of the hand. The muscle tissue can be disregarded.

Fig. 3 : Experimental setup for unconstrained shearing collision injury analysis.

Fig. 4 : Impact conditions: a) dissected specimen with collision locations and b) impact angle $\alpha$ for different impactors.

Impactor	0 kg	2×1 kg	2×2 kg	2×3 kg	2×4 kg
Wedge	1.56	3.53	5.47	7.46	9.46
Edge	1.43	3.40	5.64	7.33	9.33
Sheet	1.52	3.49	5.43	7.43	9.42

TABLE I : Effective mass (

m_{\text{eff}}

) values for different impactor types and added weights.

Injury Classification	$\mathbf{\alpha}$ = $45\text{\,}{}^{\circ}$	$\mathbf{\alpha}$ = $70\text{\,}{}^{\circ}$	Total
Skin Imprint	154	255	409
Skin Cut	27	80	107
Skin Abrasion	31	19	50
Tendon Injury	1	6	7

TABLE II : Injury occurrences by collision angle across all experiments. The table highlights differences in injury types between 45° and 70° impact configurations.

Fig. 6 : Observed percentage of injury for skin cuts occurring at $45\text{\,}{}^{\circ}$ impacts using pendulum Ia) and Ib) with $2.6\text{\,}\mathrm{k}\mathrm{g}$ and $6.4\text{\,}\mathrm{k}\mathrm{g}$ mass with all impactors.

Fig. 7 : Observed percentage of skin cuts occurring at $70\text{\,}{}^{\circ}$ impacts using pendulum Ia) and Ib) with $2.6\text{\,}\mathrm{k}\mathrm{g}$ and $6.4\text{\,}\mathrm{k}\mathrm{g}$ mass with all impactors.

Fig. 8 : Energy distribution of observed injury types using the respective impactors for Ib).

Fig. 9 : Observed Injury severity in unconstrained, shearing contacts over all impactors distributed over the kinetic energy of the pendulum in the contact.

Fig. 10 : Injury Protection Database (surrogate studies): Most conservative thresholds for avoiding occurrence of any open skin injury from surrogate studies on pig front paws.

Fig. 11 : Scenario-based threshold selection for choosing safety threshold focused on human hand and fingers.

Robot Safety

Robot safety studies how to prevent harmful physical actions while enabling robots to complete tasks around people and in uncertain environments.

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Adaptive Collision Sensitivity for Efficient and Safe Human-Robot Collaboration

Lukas Rustler, Matej Misar, Matej Hoffmann

Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

Robot Safety Human-Robot Collaboration Robot Collision Avoidance

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What is considered safe for a robot operator during physical human-robot collaboration (HRC) is specified in corresponding HRC standards (e.g., ISO 10218-2:2025). The regime that allows collisions between the moving robot and the operator, called Power and Force Limiting (PFL), restricts the permissible contact forces. Using the same fixed contact thresholds on the entire robot surface results unnecessary productivity losses, as the robot needs to stop even when impact forces are within limits. Here we present a framework that decides whether the robot should interrupt motion based on estimated collision force computed individually for different parts of the robot body and dynamically on the fly, based on the Effective Mass (EM) of each robot link. We performed experiments on a simulated and real collaborative robot arm (UR10e) with sensitive skin (AIRSKIN) for collision detection and isolation. To demonstrate the generality of our method, we added experiments on simulated KUKA LBR iiwa robot, where collision sensing draws on joint torque sensing. On a mock pick-and-place scenario with both transient and quasi-static collisions, we show an increase in productivity over 45% from using the standard approach. The method is applicable to any robot for which the EF can be calculated.

Abstract

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