Cognitive Robotics in Industrial Environments

Puls, Stephan; Graf, Jürgen; Wörn, Heinz

doi:10.5772/28130

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Monitoring and awareness of the situation is especially important for collaborative robots that work together with people in an unstructured environment (Villani et al, 2018). Contextual awareness can bring enormous benefits to collaborative robotics, as it allows us to make more human-like reasoning and consequent decision-making and ultimately adjust the behavior of the robotic system according to the context in the work area, depending on the potential situation (Puls et al, 2012;Gombolay et al, 2017). With the help of 3D cameras, we can capture the entire work area, as well as the direction and speed of movement of individual actors (robot and human) (Puls et al, 2012;Marvel, 2017).…”

Section: Situation Awarenessmentioning

confidence: 99%

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Zorman

Žlahtič

Stradovnik

et al. 2022

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PurposeCollaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human–machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields.Design/methodology/approachFirst, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline.FindingsThe identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics.Originality/valueThe authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields will increase progress and, in some cases, cut some shortcuts in achieving the aforementioned goals.

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Section: Situation Awarenessmentioning

confidence: 99%

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Zorman

Žlahtič

Stradovnik

et al. 2022

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“…In our previous research a framework for human-robot cooperation (MAROCO) was developed [1,2]. Its aim is to realize a comprehensive cognitive cycle which consists of (1) sensing, (2) cognition, and (3) acting.…”

Section: Overviewmentioning

confidence: 99%

“…We are conducting research on recognition of and reasoning about human actions, situations and spatiotemporal context in a human centered productive environment, in order to enable interactive and cooperative scenarios. For this purpose a framework for human-robot cooperation (MAROCO) was introduced [1,2], in which human pose reconstruction and context awareness is achieved (see Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Context-sensitive natural language generation for human readable event logs based on situation awareness in human-robot cooperation

Puls

Lemcke

Wörn

2014

2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

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Increasing complexity of robotic systems in respect to their abilities and reactions to environmental influences complicate the error and accident analysis. Even for experts the analysis of the vast amount of data gathered during robot operation is difficult due to the usually cryptic and unstructured form of data logs. In this work a system is introduced which analysis logs from a human-robot cooperation system and produces human readable event logs through natural language generation. For generation a template based approach is introduced which allows for adaption of detail granularity in order to facilitate different expert levels of end users. The information used during context-sensitive log generation is the result of different recognition and situation analysis methods. It is shown that the proposed system is capable of analyzing a data stream according to contextual relations and generates appropriate summarizations of occurred events.

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“…Ultrasonic sensors [4], capacitive sensors [5,6], and laser scanner systems [7] have been tried to avoid collisions. However, the information provided by these sensors does not cover the whole scene, and so these systems can only provide a limited contribution to enhance safety in human-robot collaboration tasks [8]. Moreover, geometric representations of human and robotic manipulators have been used to obtain a spatial representation in human-robot collaboration tasks.…”

Section: Collision Avoidancementioning

confidence: 99%

“…An example is a single framework for humanrobot cooperation whose purpose is to achieve a scene reconstruction of a robotic environment by markerless kinematic estimation. For example, [8,25] use the information delivered by a 3D ToF camera mounted to the top of a robotic cell. This information is employed with the purpose of extracting robust features from the scene, which are the inputs to a module that estimates risks and controls the robot.…”

Section: Range Systemsmentioning

confidence: 99%

Visual sensor fusion for active security in robotic industrial environments

Robla

Llata

Torre-Ferrero

et al. 2014

EURASIP J. Adv. Signal Process.

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This work presents a method of information fusion involving data captured by both a standard charge-coupled device (CCD) camera and a time-of-flight (ToF) camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time-of-flight information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time, this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localisation. Further development of these methods will make it possible to identify objects and their position in the real world and to use this information to prevent possible collisions between the robot and such objects.

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Cognitive Robotics in Industrial Environments

Cited by 10 publications

References 31 publications

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Context-sensitive natural language generation for human readable event logs based on situation awareness in human-robot cooperation

Visual sensor fusion for active security in robotic industrial environments

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