Within the context of Industry 4.0, the presence of robotic systems inside the production plant is increasing, thus leading to the need for appropriate safety rules enhancing human-robot interaction. However, without a systematic approach, mapping all the possible scenarios becomes critical. This paper aims at laying the foundations for a uni ed safety-based semantic approach in cooperative multi-robot industrial environments. The objective of this study is to investigate an approach to organize, store and re-use construction safety knowledge by means of de ning a new ontology and the construction of rules for safety application.
The Industry 4.0 paradigm is driving the innovation of manufacturing and
production processes [
The lack of knowledge related to the tool in use by the robot while cooperating with a human operator may result in unpredictable accidents (e.g., in the case the robot is manipulating a bulky and heavy part with a mechanical gripper while cooperating with a human operator such information is important to de ne workspace limitations, velocity etc.). Since in common industrial applications such knowledge is not yet available, robots are used under their achievable performance (in order to always ensure safety), resulting in decreasing production capabilities. The combination of the Semantic Web technologies and ontologies (that de ne concepts of a certain domain and relations between them) can enable such knowledge.
The aim of this paper is to ll this gap and present an initial work toward a uni ed knowledge framework for the de nition of safety rules in production environments by using semantic technologies. Our contributions can be summarized as follows: (i) de nition of a framework for the speci c case of safety rules; (ii) development of an ontology to describe concepts of the working space; (iii) case study related to a cooperative installation task.
This paper is organized as follows: the application scenarios are introduced in Section 2 while the state-of-the-art is presented in Section 3. The proposed framework is described in Section 4 while conclusions end the paper in Section 5. 2
More and more industries are embracing innovation on the process of assembly of their products by means of advanced human-robot collaborative solutions, i.e., i) a Lightweight Mobile Arm (LMA) to perform autonomous transportation of the parts and installation tasks, ii) an empowering robot to perform installation tasks, iii) a sensing solution for cluttered environments to identify human workers inside the working scene.
Considering such multi-robot environment, the de nition of a uni ed safety framework is mandatory. We de ne three scenarios related to the multi-robot collaborative environment.
Scenario 1 - Exoskeleton Empowering Human Worker: exoskeletons are used to relieve humans from heavy tasks, physically connected to the worker.
Scenario 2 - Empowering Robot for Cooperative Installation Task: the empowering robot is used to install bulky/heavy components (hatracks). Such manipulator, equipped with a force sensor, a mechanical gripper and a vision system (to track humans) is physically interacting with the human operator.
Scenario 3 - LMA for Autonomous Installation Task: the LMA is used to autonomously install medium-size components (e.g., side-wall panels). Such manipulator, equipped with a force sensor, a mechanical gripper and with a vision system (to track humans) should not interact with humans for this task.
A plethora of EU-funded project have investigated and are currently
investigating safety issues at di erent levels (physical interaction, workspace design, etc.).
In the Saphari project1, collision avoidance and prevention algorithms have been
developed to activate collaboration by using human gestures and voice
commands [
Standards (e.g. ISO 20218:2011 [
The integration of heterogeneous knowledge is required to ensure safety. For
instance, the combination of data coming from sensors and from the environment
where the robot is operating enables to understand the context where the robot
is working, consequently de ning appropriate behaviors and safety rules.
However, safety depends also on the speci c task assigned to the robot and to the
tools that the robot is exploiting. This paper proposes a uni ed robot
knowledge approach that considers knowledge coming from sensory data together with
context information. Taking inspiration from [
IfcProduct in ifcOWL); { SpatialElement: any spatial element that might be used to de ne a spatial structure or a spatial zones (cf. class IfcSpatialElement in ifcOWL); { Space: de ning the speci c location where the robot is working, e.g. assembly lines, storage, corridor, etc. (cf. class IfcSpace in ifcOWL); { Element: physically existent object that can be characterized by a placement and 3D representation (cf. class IfcElement in ifcOWL). { BuildingElement: any type of static element that may be an obstacle for the robot, e.g., walls, doors, etc. (cf. class IfcBuildingElement in ifcOWL); { Robot: de ning the robotic systems working inside the production plant, e.g.
mobile platforms, lightweight manipulators, exoskeletons, etc.;
{ Tool: de ning the speci c tool in use to the robot to perform the speci c
task, e.g. mechanical gripper, screwer, etc.;
{ Sensor: de ning di erent external sensors that the robot can use to perceive
the environment, e.g. vision systems, force sensors, etc. (cf. class IfcSensor
in ifcOWL, class sosa:Sensor in SSN/SOSA ontology [
The workspace is modeled by tools, the space where they are located and the context where they are used. In fact, a tool can be recognized not only by its characteristics such as shape, size, material, but also by the spatial context where it is located. Moreover, the knowledge of the robots, the sensors, the obstacles and the human operators inside the working scenario and the allocated tasks to the robots are fundamental to model the working scene. Such ontology de nition can therefore be applied to the human-robot cooperation in industrial environments, where physical and non-physical cooperation is required. 4.2
Knowledge association creates and describes the relationship between ontology classes and properties by means of axioms. Logical inference can be exploited to automatically generate new knowledge starting from generic axioms and speci c instances. Such framework enables robot to perceive the environment and the context where it is performing in such a way that is easy to avoid obstacles, collisions and to process knowledge coming from other sensors and humans. The axioms are de ned using Description Logic (DL) and can be applied also to verify that data are compliant with the ontology schema. Logic representations are de ned to identify the speci c working scene in which the robot is operating and to consequently de ne the related safety rules from the standards. Since in the scope of OWL reasoning is monotonic because of the Open World Assumption, the exploitation of other non-monotonic logic languages (e.g., ASP) will be taken in consideration to support the application scenarios. 4.3
As an example, we describe the three scenarios as in Section 2 and describe the logic representations for de ning safety rules for Scenario 2 in Section 2. Scenario 1 - Exoskeleton Empowering Human Worker: On the basis of the exoskeleton internal sensors information (e.g., encoders, torque sensors, etc.) it is possible to identify safety-critical situations (such as critical human postures). Moreover, on the basis of the knowledge of the executed task and involved tools, it is possible to on-line check for safety rules to be applied during the task execution.
Scenario 2 - Empowering Robot for Cooperative Installation Task: the complete working scene can be de ned having the information related to the tool of the robot, the knowledge about the human operator motion and the safety features implemented by the robot. The related safety rules can be then identi ed and applied. The examples described by Algorithms 1 and 2 can be exploited, considering the physical HRI installation scenario. Algorithm 1 is considering the use of a safe tool, while Algorithm 2 is considering the use of a non-safe tool. The two proposed scenarios have to apply di erent safety rules since in the second case the non-safe tool introduces higher safety risks. Scenario 3 - Autonomous Installation Task Performed by Lightweight Manipulator Since the target application is supposed to be autonomous, no physical interaction between the robot and human operators should be established during the task execution. However, the human operator may enter the working area of the manipulator. Therefore, one of the key topic de ning the safety rules to be applied in such a case is related to the possibility to track the human motion. The complete working scene can be de ned having the information related to the tool in use by the robot, the knowledge about the human operator position and the safety features implemented by the robot. In such a way, the related safety rules can be identi ed and applied. 5
In this paper a uni ed safety framework for the Industry 4.0 environment is proposed, including (i) the de nition of the framework, (ii) the development of an ontology to describe concepts of the working space, and (iii) the de nition of safety rules based on the use cases are proposed. A safety application is detailed to described the adopted approach, for assembly of heavy products in industry. The proposed ontology will be further developed, by integrating existing ontology modules and making extension in the human-robot collaboration context.
The work has been partially developed within the H2020 EUROBENCH STEPbySTEP project.