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Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.
The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models.
Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.
Ground or aerial robots equipped with advanced sensing technologies, such as three-dimensional laser scanners and advanced mapping algorithms, are deemed useful as a supporting technology for first responders. A great deal of excellent research in the field exists, but practical applications at real disaster sites are scarce. Many projects concentrate on equipping robots with advanced capabilities, such as autonomous exploration or object manipulation. In spite of this, realistic application areas for such robots are limited to teleoperated reconnaissance or search. In this paper, we investigate how well state-of-the-art and off-the-shelf components and algorithms are suited for reconnaissance in current disaster-relief scenarios. The basic idea is to make use of some of the most common sensors and deploy some widely used algorithms in a disaster situation, and to evaluate how well the components work for these scenarios. We acquired the sensor data from two field experiments, one from a disaster-relief operation in a motorway tunnel, and one from a mapping experiment in a partly closed down motorway tunnel. Based on these data, which we make publicly available, we evaluate state-of-the-art and off-the-shelf mapping approaches. In our analysis, we integrate opinions and replies from first responders as well as from some algorithm developers on the usefulness of the data and the limitations of the deployed approaches, respectively. We discuss the lessons we learned during the two missions. These lessons are interesting for the community working in similar areas of urban search and rescue, particularly reconnaissance and search.
The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.
In this paper we present an extension of the action language Golog that allows for using fuzzy notions in non-deterministic argument choices and the reward function in decision-theoretic planning. Often, in decision-theoretic planning, it is cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, even for domain experts, it is not always easy to specify a reward function. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in Golog, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy uents. In Golog’s forward-search DT planning algorithm, these formulas are evaluated in order to find the agent’s optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order.