Cognitive Technical Systems Research Articles

Role of AI in Control System Design for Robotics

In this study, the role of AI in controlling the robotic system has been discussed in a comprehensive manner. For executing this study, AI has been introduced with its features, implications and loopholes that directly affects the universal digital technology. Places and sectors that have adopted AI to bring growth have been discussed in this chapter. Nowadays different types of advanced technologies have been invented to track human body motion and other features that make people's life more easy and advanced. Here cognitive implementation of technical systems has been discussed briefly along with its loopholes that need improvement to overcome these barriers. Assistive kitchens with their equipped tools have been mentioned in this study with brief description and manufacturing and other business industries have also discussed here that adopt AI technology to bring growth. Keywords : AI, Cognitive technical system, robotics, control system, sensor, and industry.

AUTONOMOUS SYSTEMS: COGNITIVE CALCULATIONS ON THE PRINCIPLES OF THE BOUNDARY GENERALIZATIONS PARADIGM

Research in the field of Autonomous Systems focuses on the development of machines and robots that are able to perceive their environment autonomously and to interact with it like a living being. This field of research includes such areas as Autonomous Intelligent Systems, Cognitive Technical Systems, Autonomous Perception and Decision Making, Cognitive/Urgent Computation, Cyber-Physical Systems, Artificial Intelligence (AI), AI Assistants, Sense-Making Platform, Cognitive Operational Systems, Cognitive Networks/Internet, Autonomous Space Robotics, Machine Learning, Big Data Calculus, Data Science Machine Eliminates Human Intuition, and simulation. The report examines the mathematical and software support of autonomous systems. The necessity of deep intellectualization of autonomous systems for space purposes is substantiated.

Characterizing the Influence of Muscle Activity in fNIRS Brain Activation Measurements

Driving is a complex and cognitively demanding task. It is essential to assess the cognitive state of the driver in order to design cognitive technical systems that can adapt to different driver cognitive states. Our research attempts to assess these states using functional Near Infrared Spectroscopy (fNIRS) by measuring brain activity in a virtual reality driving simulator. However, the fNIRS brain activation measurements could be influenced by muscle activity and we wanted to investigate this phenomenon. For this, we designed a paradigm with two conditions (listen, teeth clench) which show a significant contrast in the influence of muscle activity. We observed that the muscle hemodynamic response can show a higher magnitude of signal change compared to brain hemodynamic response. The muscle hemodynamic response showed an increase in deoxygenated hemoglobin (HbR) whereas the brain hemodynamic response showed a decrease in HbR. Moreover, the dynamics of the brain and muscle hemodynamic response differed. The brain response showed the same latency for oxygenated hemoglobin (HbO) and HbR while the muscle HbR response had a slower latency compared to HbO. We concluded that the fNIRS brain activation measurements could indeed be influenced by muscle activity. We were also able to determine some characteristics of the muscle hemodynamic response.

Companion-Technology for Cognitive Technical Systems

We introduce the Transregional Collaborative Research Centre “Companion-Technology for Cognitive Technical Systems”—a cross-disciplinary endeavor towards the development of an enabling technology for Companion-systems. These systems completely adjust their functionality and service to the individual user. They comply with his or her capabilities, preferences, requirements, and current needs and adapt to the individual’s emotional state and ambient conditions. Companion-like behavior of technical systems is achieved through the investigation and implementation of cognitive abilities and their well-orchestrated interplay.

Fusion paradigms in cognitive technical systems for human–computer interaction

Recent trends in human–computer interaction (HCI) show a development towards cognitive technical systems (CTS) to provide natural and efficient operating principles. To do so, a CTS has to rely on data from multiple sensors which must be processed and combined by fusion algorithms. Furthermore, additional sources of knowledge have to be integrated, to put the observations made into the correct context. Research in this field often focuses on optimizing the performance of the individual algorithms, rather than reflecting the requirements of CTS. This paper presents the information fusion principles in CTS architectures we developed for Companion Technologies. Combination of information generally goes along with the level of abstractness, time granularity and robustness, such that large CTS architectures must perform fusion gradually on different levels — starting from sensor-based recognitions to highly abstract logical inferences. In our CTS application we sectioned information fusion approaches into three categories: perception-level fusion, knowledge-based fusion and application-level fusion. For each category, we introduce examples of characteristic algorithms. In addition, we provide a detailed protocol on the implementation performed in order to study the interplay of the developed algorithms.

A Cooperative Automation Concept for User-oriented Support of Air Target Identification

For over 40 years science in a variety of domains has conducted an impressive amount of research in the field of assistance and automation concepts, which apply human factors and needs in the design stage of cooperative and cognitive technical systems. Cooperative automation is an approach that tries to keep the user in the control loop by means of a continuous interaction during task execution. Cooperative automation aims to support the harmonization of the actor's activities (user and automation) in a human-machine-system. To enable this, both the internal (processing and computation) and external (communications and interfaces) design of the automation has to be compatible with human competence. This contribution addresses the key features of cooperative automation. These features have been used for creating a design space, which can be used as method repository in order to develop assistance systems based on the cognitive capabilities of the human. The key elements are represented as dimensions of the design space. Corresponding theories and methods are discussed. The design process for a cognitive and cooperative assistance system based on the dimensions of the design space are described. The developed Cognitive and Cooperative Assistance System (COGAS), has been implemented in the domain of air target identification on German naval ships. An evaluation of COGAS has been conducted with naval operators in order to verify its operability and to optimize and extend the COGAS functionality. The results show that COGAS improves the operator's performance in terms of increasing the amount of correct decisions and accelerating the identification process without increasing the operator's stress level. In addition it was found that COGAS helps to enhance the situational awareness due to the workload optimization.

Adaptive cognitive technical systems.

Adaptive cognitive technical systems are capable of sensing the internal state of its user and of adapting its behavior appropriately to those measurements to improve the usability of the system. One important example of such user state is the user's mental workload level. This paper gives an introduction to the topic of workload recognition and adaptation. It reviews the literature on recognition of workload from physiological signals and on how those user state estimates are employed to improve human-machine interaction.

Combination of sequential class distributions from multiple channels using Markov fusion networks

The recognition of patterns in real-time scenarios has become an important trend in the field of multi-modal user interfaces in human computer interaction. Cognitive technical systems aim to improve the human computer interaction by means of recognizing the situative context, e.g. by activity recognition (Ahad et al. in IEEE, 1896–1901, 2008), or by estimating the affective state (Zeng et al., IEEE Trans Pattern Anal Mach Intell 31(1):39–58, 2009) of the human dialogue partner. Classifier systems developed for such applications must operate on multiple modalities and must integrate the available decisions over large time periods. We address this topic by introducing the Markov fusion network (MFN) which is a novel classifier combination approach, for the integration of multi-class and multi-modal decisions continuously over time. The MFN combines results while meeting real-time requirements, weighting decisions of the modalities dynamically, and dealing with sensor failures. The proposed MFN has been evaluated in two empirical studies: the recognition of objects involved in human activities, and the recognition of emotions where we successfully demonstrate its outstanding performance. Furthermore, we show how the MFN can be applied in a variety of different architectures and the several options to configure the model in order to meet the demands of a distinct problem.

Perception as a key component for cognitive technical systems

Cognitive capabilities such as perception, reasoning, learning, and planning turn technical systems into systems that "know what they are doing." Starting from the human brain the Cluster of Excellence "CoTeSys" investigates cognition for technical systems such as vehicles, robots, and factories. Technical systems that are cognitive will be much easier to interact and cooperate with, and will be more robust, flexible, and efficient. For understanding their environment and interacting with humans the cognitive system's most important sense is the visual one. The talk presents recent results using the visual sensor in building environment models, self localization of autonomous moving robots, navigation, simultaneous tracking of groups of humans and robots, face detection and evaluation of gaze direction and facial expression, and emotional communication between humans and robots.

CoTeSys—Cognition for Technical Systems

The CoTeSys cluster of excellence (Beetz et al. in Proceedings of the 30th German Conference on Artificial Intelligence, KI-2007, pp. 19–42, 2007) investigates cognition for technical systems such as robots and factories. Cognitive technical systems (CTS) are information processing systems equipped with artificial sensors and actuators, integrated and embedded into physical systems, and acting in a physical world. They differ from other technical systems as they perform cognitive control and have cognitive capabilities. Cognitive control orchestrates reflexive and habitual behavior in accord with longterm intentions. Cognitive capabilities such as perception, action, knowledge and models, reasoning, learning and planning turn technical systems into systems that “know what they are doing”. The cognitive capabilities result in systems of higher reliability, flexibility, adaptivity and better performance.

Cognition in Manual Assembly

Manual assembly conducted by skilled human workers is of outstanding relevance for flexible production with high precision. Nevertheless, due to capacity limitations humans need to be supported during the working process in order to reduce mental workload and for enhancement of performance. Cognitive technical systems are able to do so by adapting the process of production to the properties of human cognitive processes which are relevant in manual assembly. During manual assembly tasks workers are confronted with various sources of information and have to switch rapidly between different tasks. The complexity of task execution can be reduced by appropriate information presentation and planning of work steps. Firstly, information processing during the working process can be supported by attentional guidance while reducing search times and accelerating assembly execution. Secondly, as there exist many possible assembly sequences for one product the optimal order of single assembly steps has to be found and interferences from previous task steps have to be minimized. The article describes two scenarios for the investigation of attention allocation as well as for the investigation of task sequences and gives a summary of results achieved so far.

Design-to-fabrication automation for the cognitive machine shop

To meet the rising demands for pure customization of products, new approaches for automated fabrication of customized part geometry are needed, on both the software and hardware side, that balance flexibility, robustness and efficiency. This is a great challenge since today it requires significant human expertise supported, only partially, by computer-aided approaches. This paper introduces a new approach and framework for an autonomous design-to-fabrication system that integrates cognitive capabilities, such as reasoning from knowledge models and autonomous planning, and embeds these in the machines themselves to automatically fabricate customized parts. The framework integrates into a common process automatic workpiece selection using an ontology, generative CNC machining planning using shape grammars and automated fixture design, based on a novel flexible fixture device hardware. Initial results are given for the machining planning approach applied to 2.5D parts with a defined approach direction and the prototyped fixture device is presented. The advantages and potential of the framework stem mainly from applying the principles of cognitive technical systems to a fabrication system to develop an integrated and on-line approach. The methods are developed specifically for use on the machine shop floor to take advantage of the possibility to update and extend knowledge models to reflect current fabrication capabilities and to adapt to changes in the environment and re-plan during operation. Finally, future directions, including integrating on-line perception and learning, are discussed, which are required to create a truly flexible and cognitive fabrication system.

System theoretic modeling of human interaction with respect to rule-based driving interactions

Modeling of human interactions allows to map the different human interactions qualitites also to Cognitive Technical Systems (CTS). Here formalized solutions can be used and integrated to the supervision of the Human-Machine-Interaction by Cognitive Interfaces. The proposed modeling approach can be used to model the human interactions to the considered part of the real world with a variety types of descriptions, such as mathematical equations, graphical networks, and algorithms. The contribution introduces the Situation-Operator-Modeling (SOM) approach theoretically and details on the representations of the approach with a rule-based algorithm with respect to driver interactions. The interaction between the human driver and the vehicle can be illustrated with a cyclic loop. Within the rule-based knowledge representation approach driver-specific variables can be defined and identified during the interaction.

Interaction of intelligent and autonomous systems – part II: realization of cognitive technical systems

In this study, the application of a meta-modelling technique to structure complex environments is demonstrated with two examples. A special situation-operator model developed to model the human-machine-interaction is applied to automated supervision within the HMI context and to realize flexible and situated interaction of autonomous systems. A concept to automated monitoring of human operators is introduced to illustrate the structuring of complex environments as the first example. In the second example, this modelling technique is applied to a mobile robot to autonomously build and update a mental model of the interaction with the environment as an example of a cognitive technical system.