Modern Artificial Intelligence (AI) research is having a huge impact in many technological domains. As in many other research areas, the application of AI in smart factories has been a key factor in the contribution to the “smartness”. Every aspect of industrial automation has been affected by the introduction of AI: the usage of AI solutions allows to introduce advanced capabilities for optimizing processes, increasing efficiency, and reducing costs. This paper analyzes some relevant aspects of the application and the impact of AI solutions on the current scenario of smart factories and industrial automation. We identify a list of significant topics related to this domain, and we report the main aspects related to them. The dissertation includes an initial quantitative analysis of the relevance of these topics in the scientific publications, a detailed description of the characteristics of the topics, and a discussion of the related challenges.

Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information.Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful 'gold standard', the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates.CLARK and Kraken (with k =31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S. aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F-measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime.We conclude that the application of nucleotide-based tools using k-mers-e.g. CLARK or Kraken-allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.

Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontology's strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.

This paper is aimed at developing optimal motion planning for a single autonomous surface vehicle (ASV) equipped with an on-board pollutant sensor that will maximize the sensor-related information available for source seeking. The ASV uses a nonlinear diffusion model of the pollutant source to estimate the intensity/level of the pollution at the present ASV location. The rate of detection of particles depends on the relative distance between the ASV and the source. First, we use a probabilistic map of the source location built through the sensor information for a dynamic motion planning of source seeking based on an entropy reduction formulation, where an appropriately defined Fisher information matrix (FIM) is used for entropy reduction or information gain. We derive the FIM for the set-up and investigate optimal trajectories. Next, we present an online nonlinear Monte Carlo algorithm that uses the obtained sensor information about pollutant at different vehicle locations to update a probabilistic uncertainty map of pollutant source location. As the mission unfolds the ASV motion is computed by considering a moving-horizon interval of decision, which will allow for the inclusion of new information available for optimal motion planning. The proposed motion planning approach is extended to take into account external disturbances and it is able to minimize the uncertainty in the pollutant source. Finally, we provide two case studies to demonstrate efficacy of the proposed motion planning algorithm.

In this paper, a hybrid algorithm for multiple-robot SLAM is proposed that combines the advantages of particle filtering and map merging. The proposed algorithm does not rely on rendezvous and calculates the unknown relative poses from the local maps of the robots. As another contribution, the uncertainty of the relative poses is taken into account by propagating the uncertainty to the past and future information using a novel algorithm. Moreover, once the relative poses are known, the integration of the information from all robots is performed using a novel batch-mode algorithm, which is a fast and efficient approach to deal with the time complexity problem. The experimental results show the effectiveness of the proposed algorithms.

This work addresses the problem of localizing a ground target observed by multiple heterogeneous unmanned vehicles. Specifically, the case of a team of unmanned aerial and ground vehicles is analyzed. Effective collaboration between unmanned aerial and ground vehicles can utilize the strengths of both platforms while mediating their individual weaknesses. In this research, a probabilistic framework is proposed to improve target localization accuracy by utilizing the epipolar geometry and inter-robot localization measurements. A virtual simulation environment is implemented to fully test the proposed methods. Hardware experiments are carried out to validate the proposed methods.

This paper presents an overview of our research on accounting for robot pose uncertainty in area coverage applications. In the vast majority of existing literature on robotics area coverage, the location uncertainty of the robot is not considered. An uncertain robot pose results in an uncertain sensor swath, which in turn creates uncertainty about the achieved coverage. Here, we present a general framework where pose estimates are mapped through the coverage sensor model to obtain a probability of coverage over the discretized workspace. This probabilistic representation can then be used to adaptively plan paths for coverage based on an entropy reduction formulation. This framework is particularly well-suited to autonomous underwater vehicles (AUVs) performing seabed surveying operations. The AUV position estimate diverges from the actual AUV position while submerged due to the lack of a global position reference. This discrepancy can result in parts of the seabed being missed, which is unacceptable in safety-critical missions such as mine countermeasures. The proposed information-based path planning approach is able to guarantee area coverage even in the case of severe AUV position estimate drift. In-water experiments with an AUV show the effectiveness of the method.

Area coverage with an onboard sensor is an important task for an unmanned aerial vehicle (UAV) with many applications. Autonomous fixed-wing UAVs are more appropriate for larger scale area surveying since they can cover ground more quickly. However, their non-holonomic dynamics and susceptibility to disturbances make sensor coverage a challenging task. Most previous approaches to area coverage planning are offline and assume that the UAV can follow the planned trajectory exactly. In this paper, this restriction is removed as the aircraft maintains a coverage map based on its actual pose trajectory and makes control decisions based on that map. The aircraft is able to plan paths in situ based on sensor data and an accurate model of the on-board camera used for coverage. An information theoretic approach is used that selects desired headings that maximize the expected information gain over the coverage map. In addition, the branch entropy concept previously developed for autonomous underwater vehicles is extended to UAVs and ensures that the vehicle is able to achieve its global coverage mission. The coverage map over the workspace uses the projective camera model and compares the expected area of the target on the ground and the actual area covered on the ground by each pixel in the image. The camera is mounted on a two-axis gimbal and can either be stabilized or optimized for maximal coverage. Hardware-in-the-loop simulation results and real hardware implementation on a fixed-wing UAV show the effectiveness of the approach. By including the already developed automatic takeoff and landing capabilities, we now have a fully automated and robust platform for performing aerial imagery surveys.

Simultaneous Localization and Mapping, or SLAM, is required for mobile robots to be able to explore prior unknown space without a global positioning reference. While multiple robots can achieve the exploration task more quickly, this benefit comes with the cost of added complexity. Probabilistic occupancy grid maps from multiple agents must be merged in real-time without any prior knowledge of their relative transformation. In addition, the probabilistic information of the maps must be accounted for and fused accordingly. In this paper, a probabilistic version of the Generalized Voronoi Diagram (GVD), called the PGVD, is used to determine the relative transformation between maps and fuse them. The new method is effective for finding relative transformations quickly and reliably. In addition, the novel approach accounts for all map uncertainties in the fusion process.

One of the major problems for multi-robot SLAM is that the robots only know their positions in their own local coordinate frames, so fusing map data can be challenging. In this research, the mapping process is extended to multiple robots with a novel occupancy grid map fusion algorithm. Map fusion is achieved by transforming individual maps into the Hough space where they are represented in an abstract form. Properties of the Hough transform are used to find the common regions in the maps, which are then used to calculate the unknown transformation between the maps. Results are shown from tests performed on benchmark data sets and real-world experiments with multiple robotic platforms.

At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are pre-planned by operators using ladder or zig-zag paths. Such surveys are often conducted with side-looking sonar sensors whose performance is dependant on a number of environment factors, as well as lateral range from the AUV track. This research presents a sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned adaptively using a multi-objective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that often produces shorter paths than conventional means and is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method.

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions.

This article discusses a newly formed IEEE-RAS working group entitled Ontologies for Robotics and Automation (ORA). The goal of this working group is to develop a standard ontology and associated methodology for knowledge representation and reasoning in robotics and automation, together with the representation of concepts in an initial set of application domains. The standard provides a unified way of representing knowledge and provides a common set of terms and definitions, allowing for unambiguous knowledge transfer among any group of humans, robots, and other artificial systems. In addition to describing the goal and structure of the group, this article gives some examples of how the ontology, once developed, can be used by applications such as industrial kitting.

In this research, a decentralized platform for SLAM with multiple robots has been developed. An EKF-based single-robot SLAM is extended to multiple-robot SLAM with a novel occupancy grid map fusion algorithm. Map fusion is achieved through a multi-step process that includes image preprocessing, segmentation, cross correlation, approximating the relative transformation matrix, tuning of the transformation through the Radon image transform and similarity index, and then verification of the result using either map entropy or a verification index. Results are shown from tests performed in a real environment with multiple robotic platforms.

In this paper, a decentralized platform for Simultaneous Localization and Mapping (SLAM) with multiple robots is developed. A novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multi-step process that includes image pre-processing, map learning, relative transformation extraction and then verification of the results. The proposed map learning method is a process based on the Self Organizing Map (SOM). In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.

In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.

In this research, a fuzzy logic controller is designed for vision-based autonomous road-following. Because of its high-speed response, portability, and flexibility, a field programmable gate array is applied to implement this control system. Furthermore, a novel hardware/software partitioning method using the genetic algorithm is developed. This method is capable of finding the tradeoff among several evaluation factors under conditions of hard constraints. A small-scaled intelligent vehicle, which is capable of autonomous road-following is built and the proposed controller is tested in the real-world environment. Experiments of hardware implementation and codesign implementation are presented and compared.

Recently, a coordinated hybrid agent (CHA) framework was proposed for the control of multiagent systems (MASs). It was demonstrated that an intelligent planner can be designed for the CHA framework to automatically generate desired actions for multiple robots in an MAS. However, in previous studies, only static obstacles in the workspace were considered. In this paper, a neural-network-based approach is proposed for a multirobot system with moving obstacles. A biologically inspired neural-network-based intelligent planner is designed for the coordination of MASs. A landscape of the neural activities for all neurons of a CHA agent contains information about the agent's local goal and moving obstacles. The proposed approach is able to plan the paths for multiple robots while avoiding moving obstacles. The proposed approach is simulated using both Matlab and Vortex. The Vortex module executes control commands from the control system module, and provides the outputs describing the vehicle state and terrain information, which are, in turn, used in the control module to produce the control commands. Simulation results show that the developed intelligent planner of the CHA framework can control a large complex system so that coordination among agents can be achieved.

In this paper, a framework is proposed for the distributed control and coordination of multiagent systems (MASs). In the proposed framework, the control of MASs is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a coordinated hybrid agent, which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input, and workspace input. In the proposed framework, we describe the coordination mechanism in a domain-independent way, i.e., as simple abstract primitives in a coordination rule base for certain dependence relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision making, and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. To verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous MASs are implemented. The proposed framework is applied to a multicrane system, a multiple robot system, and a MAS consisting of an overhead crane, a mobile robot, and a robot manipulator. It is demonstrated that the proposed framework can model the three MASs. The agents in these systems are able to cooperate and coordinate to achieve a global goal. In addition, the stability of systems modeled using the proposed framework is also analyzed.

In this research, we introduce FPGA based fuzzy logic controller (FLC). The benefit of using FPGA based FLC compare to software FLC is that the computation time reduction. Using this FLC, we design automated car back parallel parking system also with complete FPGA based controller. We build a small-scaled robot car and test on a real environment with VHDL code for wall following and parking. This paper describes the background of fuzzy logic system, the design of fuzzy logic system with FPGA and the experimental results.

The coordinated hybrid agent (CHA) framework for the control of multi-agent systems (MASs) has been used to model both the homogeneous and the heterogeneous multi-agent systems. In this framework, the control of the MASs is regarded as a decentralized control and coordination of agents. The CHA framework is able to implement coordination tasks for multi-agent systems. In this study, the optimization of MASs modelled by the CHA framework is studied. The time-driven dynamics and the event-driven dynamics for the optimization of a CHA system are given. The optimization problem of the MASs is analyzed. An example is also given to illustrate how to define the optimization problem for a CHA. The forward algorithm is also introduced for solving the optimization problem for a CHA system.