Cognitive Task Planning for Smart Industrial Robots

This research work presents a novel Cognitive Task Planning framework for Smart Industrial Robots. The framework makes an industrial mobile manipulator robot Cognitive by applying Semantic Web Technologies. It also introduces a novel Navigation Among Movable Obstacles algorithm for robots navigating and manipulating inside a firm. The objective of Industrie 4.0 is the creation of Smart Factories: modular firms provided with cyber-physical systems able to strong customize products under the condition of highly flexible mass-production. Such systems should real-time communicate and cooperate with each other and with humans via the Internet of Things. They should intelligently adapt to the changing surroundings and autonomously navigate inside a firm while moving obstacles that occlude free paths, even if seen for the first time. At the end, in order to accomplish all these tasks while being efficient, they should learn from their actions and from that of other agents. Most of existing industrial mobile robots navigate along pre-generated trajectories. They follow ectrified wires embedded in the ground or lines painted on th efloor. When there is no expectation of environment changes and cycle times are critical, this planning is functional. When workspaces and tasks change frequently, it is better to plan dynamically: robots should autonomously navigate without relying on modifications of their environments. Consider the human behavior: humans reason about the environment and consider the possibility of moving obstacles if a certain goal cannot be reached or if moving objects may significantly shorten the path to it. This problem is named Navigation Among Movable Obstacles and is mostly known in rescue robotics. This work transposes the problem on an industrial scenario and tries to deal with its two challenges: the high dimensionality of the state space and the treatment of uncertainty. The proposed NAMO algorithm aims to focus exploration on less explored areas. For this reason it extends the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. The extension does not impose obstacles avoidance: it assigns an importance to each cell by combining the efforts necessary to reach it and that needed to free it from obstacles. The obtained algorithm is scalable because of its independence from the size of the map and from the number, shape, and pose of obstacles. It does not impose restrictions on actions to be performed: the robot can both push and grasp every object. Currently, the algorithm assumes full world knowledge but the environment is reconfigurable and the algorithm can be easily extended in order to solve NAMO problems in unknown environments. The algorithm handles sensor feedbacks and corrects uncertainties. Usually Robotics separates Motion Planning and Manipulation problems. NAMO forces their combined processing by introducing the need of manipulating multiple objects, often unknown, while navigating. Adopting standard precomputed grasps is not sufficient to deal with the big amount of existing different objects. A Semantic Knowledge Framework is proposed in support of the proposed algorithm by giving robots the ability to learn to manipulate objects and disseminate the information gained during the fulfillment of tasks. The Framework is composed by an Ontology and an Engine. The Ontology extends the IEEE Standard Ontologies for Robotics and Automation and contains descriptions of learned manipulation tasks and detected objects. It is accessible from any robot connected to the Cloud. It can be considered a data store for the efficient and reliable execution of repetitive tasks; and a Web-based repository for the exchange of information between robots and for the speed up of the learning phase. No other manipulation ontology exists respecting the IEEE Standard and, regardless the standard, the proposed ontology differs from the existing ones because of the type of features saved and the efficient way in which they can be accessed: through a super fast Cascade Hashing algorithm. The Engine lets compute and store the manipulation actions when not present in the Ontology. It is based on Reinforcement Learning techniques that avoid massive trainings on large-scale databases and favors human-robot interactions. The overall system is flexible and easily adaptable to different robots operating in different industrial environments. It is characterized by a modular structure where each software block is completely reusable. Every block is based on the open-source Robot Operating System. Not all industrial robot controllers are designed to be ROS-compliant. This thesis presents the method adopted during this research in order to Open Industrial Robot Controllers and create a ROS-Industrial interface for them

Metody stosowania wiedzy dziedzinowej do poprawiania jakości klasyfikatorów

The dissertation deals with methods that allow the use of domain knowledge to improve the quality of classifiers, where quality improvement concerns: feature extraction methods, classifier construction methods, and methods for predicting decision values for new objects. In particular the following methods have been proposed to improve the quality of classifiers: the expert features (attributes) defined using domain knowledge expressed in a language that uses the temporal logic, a new method of measuring the quality of cuts during supervised discretization using a matrix of the distances between decision attribute values defined by a domain knowledge, a new decision tree that uses redundant cuts to verify the partition of a tree node, a new method for determination of similarities between objects (e.g. patients) using an ontology defined by an expert with its application to the k-nearest neighbors classifier construction and a new method for generating cross rules describing the effect of a factor interfering perception based on a classifier. All of the aforementioned methods have been implemented in the CommoDM software library, which is one of the RSES-lib library extensions. Implemented methods have been tested on real data sets. These were comparative data sets known from the literature as well as own medical data sets collected during the preparation of the dissertation. The latter data sets are associated with the medical aspect of the dissertation that deals with the support of treatment of patients with stable ischemic heart disease, and the main medical problem considered in the thesis is the problem of predicting the presence of significant coronary artery stenosis based on non-invasive heart monitoring by Holter method. The results of experiments confirm the effectiveness of the application of additional domain knowledge in the task of creating and testing classifiers, because after the application of new methods the quality of classifiers has increased considerably. At the same time, the clinical interpretation of the results is more consistent with medical knowledge. The research has been supported by the grant DEC-2013/09/B/ST6/01568 and the grant DEC-2013/09/B/NZ5/00758, both from the National Science Centre of the Republic of Poland. Their results were published in 10 publications, including 3 publications in journals from the A list of the Polish Ministry of Science and Higher Education, 3 publications indexed in the Web of Science, one chapter in a monograph and 3 post-conference publications

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences