Multirobot sensor-based coverage path planning determines a tour for each robot in a team such that every point in a given workspace is covered by at least one robot using its sensors. In sensor-based coverage of narrow spaces, i.e., obstacles lie within the sensor range, a generalized Voronoi diagram (GVD)-based graph can be used to model the environment. A complete sensor-based coverage path plan for the robot team can be obtained by using the capacitated arc routing problem solution methods on the GVD-based graph. Unlike capacitated arc routing problem, sensor-based coverage problem requires to consider two types of edge demands. Therefore, modified Ulusoy algorithm is used to obtain mobile robot tours by taking into account two different energy consumption cases during sensor-based coverage. However, due to the partially unknown nature of the environment, the robots may encounter obstacles on their tours. This requires a replanning process that considers the remaining energy capacities and the current positions of the robots. In this paper, the modified Ulusoy algorithm is extended to incorporate this dynamic planning problem. A dynamic path-planning approach is proposed for multirobot sensor-based coverage of narrow environments by considering the energy capacities of the mobile robots. The approach is tested in a laboratory environment using Pioneer 3-DX mobile robots. Simulations are also conducted for a larger test environment.

Early fault detection and real-time condition monitoring systems have become quite significant for today’s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers’ cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system’s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.

Localization is an important problem for robotics and mobile platforms. Although there is some globally accepted positioning systems (e.g., GPS, GLONASS) for outdoor environments, there is not such a system for indoor. In this study, an ultrasonic based positioning system (SESKON) is developed especially for indoor robotic applications. The SESKON uses only ultrasonic signals, so it differs from other positioning systems that are developed for indoor. The SESKON uses Time Difference of Arrival (TDOA) technique for position calculation. TDOA is preferred since it does not require clock synchronization between receiver and transmitters. Tests are conducted to show the effectiveness of the developed SESKON system.