Cognitive Reasoning for Compliant Robot Manipulation

Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

ROSAPL: towards a heterogeneous multi‐robot system and Human interaction framework

The appearance of numerous robotic frameworks and middleware has provided researchers with reliable hardware and software units avoiding the need of developing ad-hoc platforms and focus their work on how improve the robots' high-level capabilities and behaviours. Despite this none of these are facilitating frameworks considering social capabilities as a factor in robots design. In a world that everyday seems more and more connected, with the slow but steady advance of the Internet of Things to many aspects of our daily lifes, the lack of social capabilities in a robot limits developers and researchers on areas where robots are seen as part of a solution, and not the solution. This thesis states that a social layer should be accessible in any robotic platform in order to ease the development of systems where such platforms are just a piece in the whole socio-technical system. As result of this e ort we present the ROSAPL framework to develop social robots on top of ROS middleware. We tested our approach in a real scenario at IBEC's Robotics group in the context of the InHANDS, which project tries to assist a handicapped persons in the kitchen. For them we designed and implemented a prototype to proof ROSAPL applicability. This latter will be fully implemented to o er real functionalities for the kitchen

Combining task and motion planning for mobile manipulators

Aplicat embargament des de la data de defensa fins el dia 31/12/2019Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit d’Enginyeria IndustrialThis thesis addresses the combination of task and motion planning which deals with different types of robotic manipulation problems. Manipulation problems are referred to as mobile manipulation, collaborative multiple mobile robots tasks, and even higher dimensional tasks (like bi-manual robots or mobile manipulators). Task and motion planning problems needs to obtain a geometrically feasible manipulation plan through symbolic and geometric search space. The combination of task and motion planning levels has emerged as a challenging issue as the failure leads robots to dead-end tasks due to geometric constraints. In addition, task planning is combined with physics-based motion planning and information to cope with manipulation tasks in which interactions between robots and objects are required, or also a low-cost feasible plan in terms of power is looked for. Moreover, combining task and motion planning frameworks is enriched by introducing manipulation knowledge. It facilitates the planning process and aids to provide the way of executing symbolic actions. Combining task and motion planning can be considered under uncertain information and with human-interaction. Uncertainty can be viewed in the initial state of the robot world or the result of symbolic actions. To deal with such issues, contingent-based task and motion planning is proposed using a perception system and human knowledge. Also, robots can ask human for those tasks which are difficult or infeasible for the purpose of collaboration. An implementation framework to combine different types of task and motion planning is presented. All the required modules and tools are also illustrated. As some task planning algorithms are implemented in Prolog or C++ languages and our geometric reasoner is developed in C++, the flow of information between different languages is explained.Aquesta tesis es centra en les eines de planificació combinada a nivell de tasca i a nivell de moviments per abordar diferents problemes de manipulació robòtica. Els problemes considerats són de navegació de robots mòbil enmig de obstacles no fixes, tasques de manipulació cooperativa entre varis robots mòbils, i tasques de manipulació de dimensió més elevada com les portades a terme amb robots bi-braç o manipuladors mòbils. La planificació combinada de tasques i de moviments ha de cercar un pla de manipulació que sigui geomètricament realitzable, a través de d'un espai de cerca simbòlic i geomètric. La combinació dels nivells de planificació de tasca i de moviments ha sorgit com un repte ja que les fallades degudes a les restriccions geomètriques poden portar a tasques sense solució. Addicionalment, la planificació a nivell de tasca es combina amb informació de la física de l'entorn i amb mètodes de planificació basats en la física, per abordar tasques de manipulació en les que la interacció entre el robot i els objectes és necessària, o també si es busca un pla realitzable i amb un baix cost en termes de potència. A més, el marc proposat per al combinació de la planificació a nivell de tasca i a nivell de moviments es millora mitjançant l'ús de coneixement, que facilita el procés de planificació i ajuda a trobar la forma d'executar accions simbòliques. La combinació de nivells de planificació també es pot considerar en casos d'informació incompleta i en la interacció humà-robot. La incertesa es considera en l'estat inicial i en el resultat de les accions simbòliques. Per abordar aquest problema, es proposa la planificació basada en contingències usant un sistema de percepció i el coneixement de l'operari humà. Igualment, els robots poden demanar col·laboració a l'operari humà per a que realitzi aquelles accions que són difícils o no realitzables pel robot. Es presenta també un marc d'implementació per a la combinació de nivells de planificació usant diferents mètodes, incloent tots els mòduls i eines necessàries. Com que alguns algorismes estan implementats en Prolog i d'altres en C++, i el mòdul de raonament geomètric proposat està desenvolupat en C++, es detalla el flux d'informació entre diferents llenguatges.Award-winningPostprint (published version

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Enhancing maritime defence and security through persistently autonomous operations and situation awareness systems

This thesis is concerned with autonomous operations with Autonomous Underwater Vehicles(AUVs) and maritime situation awareness in the context of enhancing maritime defence and security. The problem of autonomous operations with AUVs is one of persistence. That is, AUVs get stuck due to a lack of cognitive ability to deal with a situation and require intervention from a human operator. This thesis focuses on addressing vehicle subsystem failures and changes in high level mission priorities in a manner that preserves autonomy during Mine Counter measures (MCM) operations in unknown environments. This is not a trivial task. The approach followed utilizes ontologies for representing knowledge about the operational environment, the vehicle as well as mission planning and execution. Reasoning about the vehicle capabilities and consequently the actions it can execute is continuous and occurs in real time. Vehicle component faults are incorporated into the reasoning process as a means of driving adaptive planning and execution. Adaptive planning is based on a Planning Domain Definition Language (PDDL) planner. Adaptive execution is prioritized over adaptive planning as mission planning can be very demanding in terms of computational resources. Changes in high level mission priorities are also addressed as part of the adaptive planning behaviour of the system. The main contribution of this thesis regarding persistently autonomous operations is an ontological framework that drives an adaptive behaviour for increasing persistent autonomy of AUVs in unexpected situations. That is, when vehicle component faults threaten to put the mission at risk and changes in high level mission priorities should be incorporated as part of decision making. Building maritime situation awareness for maritime security is a very difficult task. High volumes of information gathered from various sources as well as their efficient fusion taking into consideration any contradictions and the requirement for reliable decision making and (re)action under potentially multiple interpretations of a situation are the most prominent challenges. To address those challenges and help alleviate the burden from humans which usually undertake such tasks, this thesis is concerned with maritime situation awareness built with Markov Logic Networks(MLNs) that support humans in their decision making. However, commonly maritime situation awareness systems rely on human experts to transfer their knowledge into the system before it can be deployed. In that respect, a promising alternative for training MLNs with data is presented. In addition, an in depth evaluation of their performance is provided during which the significance of interpreting an unfolding situation in context is demonstrated. To the best of the author’s knowledge, it is the first time that MLNs are trained with data and evaluated using cross validation in the context of building maritime situation awareness for maritime security

A proposal for a global task planning architecture using the RoboEarth cloud based framework

As robotic systems become more and more capable of assisting in human domains, methods are sought to compose robot executable plans from abstract human instructions. To cope with the semantically rich and highly expressive nature of human instructions, Hierarchical Task Network planning is often being employed along with domain knowledge to solve planning problems in a pragmatic way. Commonly, the domain knowledge is specific to the planning problem at hand, impeding re-use. Therefore this paper conceptualizes a global planning architecture, based on the worldwide accessible RoboEarth cloud framework. This architecture allows environmental state inference and plan monitoring on a global level. To enable plan re-use for future requests, the RoboEarth action language has been adapted to allow semantic matching of robot capabilities with previously composed plans

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version