Control Architecture Concepts and Properties of an Ontology Devoted to Exchanges in Mobile Robotics

National audienceA specific ontology is proposed in the scope of the development of a platform devoted to exchanges between academics and industrials of the robotic domain. This paper presents the tools used for knowledge elicitation, the concepts and properties linked with control architecture, the use of the resulting ontology for description of some scenarios and the tracks for the development of a domain specific language grounded on the ontology. Knowledge elicitation is performed in web ontology language thanks to Protégé ontology editor. The ontology is structured as a set of modules organized around a kernel. Modules addressing systems, information, robot and mission include concepts and properties for control architecture description. The expressivity of the ontology is demonstrated describing architectures for a set of scenarios; urban robotic scenario, air-ground scenario, landmark search scenario and military unmanned aerial vehicles scenario. Finally some tracks for the use of the ontology for developing a domain specific language are given

A review and comparison of ontology-based approaches to robot autonomy

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft

An ontology system for rehabilitation robotics

Representing the available information about rehabilitation robots in a structured form, like ontologies, facilitates access to various kinds of information about the existing robots, and thus it is important both from the point of view of rehabilitation robotics and from the point of view of physical medicine. Rehabilitation robotics researchers can learn various properties of the existing robots and access to the related publications to further improve the state-of-the-art. Physical medicine experts can find information about rehabilitation robots and related publications (possibly including results of clinical studies) to better identify the right robot for a particular therapy or patient population. Therefore, considering also the advantages of ontologies and ontological reasoning, such as interoperability of various heterogenous knowledge resources (e.g., patient databases or disease ontologies), such an ontology provides the underlying mechanisms for translational physical medicine, from bench-to-bed and back, and personalized rehabilitation robotics. In this thesis, we introduce the first formal rehabilitation robotics ontology, called RehabRobo-Onto, to represent information about rehabilitation robots and their properties. We have designed and developed RehabRobo-Onto in OWL, collaborating with experts in robotics and in physical medicine. We have also built a software (called RehabRobo- Query) with an easy-to-use intelligent user-interface that allows robot designers to add/modify information about their rehabilitation robots to/from RehabRobo-Onto. With RehabRobo-Query, the experts do not need to know about the logic-based ontology languages, or have experience with the existing Semantic Web technologies or logic-based ontological reasoners. RehabRobo-Query is made available on the cloud, utilizing Amazon Web services, so that rehabilitation robot designers around the world can add/modify information about their robots in RehabRobo-Onto, and rehabilitation robot designers and physical medicine experts around the world can access the knowledge in RehabRobo-Onto by means of questions about robots, in natural language, with the guide of the intelligent userinterface of RehabRobo-Query. The ontology system consisting of RehabRobo-Onto and RehabRobo- Query is of great value to robot designers as well as physical therapists and medical doctors. On the one hand, robot designers can access various properties of the existing robots and to the related publications to further improve the state-of-the-art. On the other hand, physical therapists and medical doctors can utilize the ontology to compare rehabilitation robots and to identify the ones that serve best to cover their needs, or to evaluate the effects of various devices for targeted joint exercises on patients with specific disorders

Cascading Verification: An Integrated Method for Domain-Specific Model Checking

Model checking is an established formal method for verifying the desired behavioral properties of system models. But popular model checkers tend to support low-level modeling languages that require intricate models to represent even the simplest systems. Modeling complexity arises in part from the need to encode domain knowledge, including domain objects and concepts, and their relationships, at relatively low levels of abstraction. We will demonstrate that, once formalized, domain knowledge can be reused to enhance the abstraction level of model and property specifications, and the effectiveness of probabilistic model checking. This thesis describes a novel method for domain-specific model checking called cascading verification. The method uses composite reasoning over high-level system specifications and formalized domain knowledge to synthesize both low-level system models and the behavioral properties that need to be verified with respect to those models. In particular, model builders use a high-level domain-specific language (DSL) to encode system specifications that can be analyzed with model checking. Domain knowledge is encoded in the Web Ontology Language (OWL), the Semantic Web Rule Language (SWRL) and Prolog, which are combined to overcome their individual limitations. Synthesized models and properties are analyzed with the probabilistic model checker PRISM. Cascading verification is illustrated with a prototype system that verifies the correctness of uninhabited aerial vehicle (UAV) mission plans. An evaluation of this prototype reveals non-trivial reductions in the size and complexity of input system specifications compared to the artifacts synthesized for PRISM

Simultaneous localisation and mapping with prior information

This thesis is concerned with Simultaneous Localisation and Mapping (SLAM), a technique by which a platform can estimate its trajectory with greater accuracy than odometry alone, especially when the trajectory incorporates loops. We discuss some of the shortcomings of the "classical" SLAM approach (in particular EKF-SLAM), which assumes that no information is known about the environment a priori. We argue that in general this assumption is needlessly stringent; for most environments, such as cities some prior information is known. We introduce an initial Bayesian probabilistic framework which considers the world as a hierarchy of structures, and maps (such as those produced by SLAM systems) as consisting of features derived from them. Common underlying structure between features in maps allows one to express and thus exploit geometric relations between them to improve their estimates. We apply the framework to EKF-SLAM for the case of a vehicle equipped with a range-bearing sensor operating in an urban environment, building up a metric map of point features, and using a prior map consisting of line segments representing building footprints. We develop a novel method called the Dual Representation, which allows us to use information from the prior map to not only improve the SLAM estimate, but also reduce the severity of errors associated with the EKF. Using the Dual Representation, we investigate the effect of varying the accuracy of the prior map for the case where the underlying structures and thus relations between the SLAM map and prior map are known. We then generalise to the more realistic case, where there is "clutter" - features in the environment that do not relate with the prior map. This involves forming a hypothesis for whether a pair of features in the SLAMstate and prior map were derived from the same structure, and evaluating this based on a geometric likelihood model. Initially we try an incrementalMultiple Hypothesis SLAM(MHSLAM) approach to resolve hypotheses, developing a novel method called the Common State Filter (CSF) to reduce the exponential growth in computational complexity inherent in this approach. This allows us to use information from the prior map immediately, thus reducing linearisation and EKF errors. However we find that MHSLAM is still too inefficient, even with the CSF, so we use a strategy that delays applying relations until we can infer whether they apply; we defer applying information from structure hypotheses until their probability of holding exceeds a threshold. Using this method we investigate the effect of varying degrees of "clutter" on the performance of SLAM

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Semantische Objektmodellierung mittels multimodaler Interaktion

Ein Konzept für eine interaktive semantische Objektmodellierung wird vorgeschlagen. Die flexible und erweiterbare Objektrepräsentation ermöglicht die Modellierung funktionaler und semantischer Objektinformationen durch die Darstellung von Eigenschaften, die menschliche Begriffe und Kategorien abbilden und die Verbindung von Objekten mit Handlungen und mit sensoriell erfassbaren Attributen herstellen. Das interaktive Modellierungssystem erlaubt die intuitive Erstellung semantischer Objektmodelle

Semantic based task planning for domestic service robots

Task Planning is developed for an autonomous mobile robot in order to support the robot to accomplish tasks in various degrees of environmental complexity. This environment can be fixed or deterministic (as in a factory), dynamic (as in the human domestic household), or non-deterministic (as in the space exploration). The robot should be provided with a reliable planning system in order to face its major challenge of being certain that its plan to accomplish a task is generated correctly, regardless of the dynamic or uncertain elements of its environment. This thesis is focused on providing the robot task planner with the ability to generate its plans reliably and detect the failures in generating correct plans. Previous approaches for generating plans depended mainly on action effects (explicit effects) that are encoded in the action model. This means that the action effects should cover most of the characteristics of the newly generated world state. However, this extra information can complicate the action model, especially in the real world. In this thesis, a semantic knowledge base is proposed to derive and check implicit information about the effects of actions during plan generation. For example, this approach would inform the robot, that it had entered a bedroom because it has recorded at least one bed and zero ovens. When a robot enters a room, the implicit expectations are derived from a semantic knowledge base about that type of room. These expectations should be verified in order to make sure the robot is in the correct room. The main contributions of this thesis are as follows: The concept of using the Semantic Knowledge Base (SKB) to support the robot task planner under deterministic conditions has been defined. A new model of high-level robot actions has been developed, and this model represents the details of robot action as ontology. This model is thus known as the Semantic Action Model (SAM). An algorithm that integrates SKB and SAMs has also been developed. This algorithm creates the “planning domain” in the Planning Domain Definition Language (PDDL) style. This is used as input to the planner to generate the plan for robot tasks. Then, a general purpose planning algorithm has also been defined, which can support planning under deterministic conditions, and is based on using ontology to represent SKB. ii A further contribution relates to the development of a probabilistic approach to deal with uncertainty in semantic knowledge based task planning. This approach shows how uncertainties in action effects and world states are taken into account by the planning system. This contribution also served to resolve situations of confusion in finding an object relevant to the successful generation of an action during task planning. The accuracy related to this type of planning in navigation scenario, on average, is (90.10%). An additional contribution is using the planning system to respond to unexpected situations which are caused by lack of information. This contribution is formalised as a general approach that models cases of incomplete information as a planning problem. This approach includes a sequence of steps for modelling and generating a plan of actions to collect the necessary information from the knowledge base to support the robot planner in generating its plan. This results in developing a new type of action which is known as a Semantic Action Model for Information Gathering (SAM_IG). These actions have the ability to access the knowledge base to retrieve the necessary information to support the planning system when it is faced with incomplete information. The information gathering approach is also used to gather the necessary information in order to check the implicit expectations of the generated actions. The correct classification related to this type of planning in navigation scenario, on average, is (92.83 %). Another contribution is concerned with solving the problem of missing information, which is using the methods for measuring concept similarity in order to extend the robot world state with new similar objects to the original one in the action model. This results in developing Semantic Realisation and Refreshment Module (SRRM) which has the ability to estimate the similarities between objects and the quality of the alternative plans. The quality of the alternative plans could be similar to the original plan, in average, 92.1%. The results reported in this thesis have been tested and verified in simulation experiments under the Robot Operating System (ROS) middleware. The performance of the planning system has been evaluated by using the planning time and other known metrics. These results show that using semantic knowledge can lead to high performance and reliability in generating robot plans during its operatio