Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application

Interpretable task planning and learning for autonomous robotic surgery with logic programming

This thesis addresses the long-term goal of full (supervised) autonomy in surgery, characterized by dynamic environmental (anatomical) conditions, unpredictable workflow of execution and workspace constraints. The scope is to reach autonomy at the level of sub-tasks of a surgical procedure, i.e. repetitive, yet tedious operations (e.g., dexterous manipulation of small objects in a constrained environment, as needle and wire for suturing). This will help reducing time of execution, hospital costs and fatigue of surgeons during the whole procedure, while further improving the recovery time for the patients. A novel framework for autonomous surgical task execution is presented in the first part of this thesis, based on answer set programming (ASP), a logic programming paradigm, for task planning (i.e., coordination of elementary actions and motions). Logic programming allows to directly encode surgical task knowledge, representing emph{plan reasoning methodology} rather than a set of pre-defined plans. This solution introduces several key advantages, as reliable human-like interpretable plan generation, real-time monitoring of the environment and the workflow for ready adaptation and failure recovery. Moreover, an extended review of logic programming for robotics is presented, motivating the choice of ASP for surgery and providing an useful guide for robotic designers. In the second part of the thesis, a novel framework based on inductive logic programming (ILP) is presented for surgical task knowledge learning and refinement. ILP guarantees fast learning from very few examples, a common drawback of surgery. Also, a novel action identification algorithm is proposed based on automatic environmental feature extraction from videos, dealing for the first time with small and noisy datasets collecting different workflows of executions under environmental variations. This allows to define a systematic methodology for unsupervised ILP. All the results in this thesis are validated on a non-standard version of the benchmark training ring transfer task for surgeons, which mimics some of the challenges of real surgery, e.g. constrained bimanual motion in small space

An architecture for adaptive task planning in support of IoT-based machine learning applications for disaster scenarios

The proliferation of the Internet of Things (IoT) in conjunction with edge computing has recently opened up several possibilities for several new applications. Typical examples are Unmanned Aerial Vehicles (UAV) that are deployed for rapid disaster response, photogrammetry, surveillance, and environmental monitoring. To support the flourishing development of Machine Learning assisted applications across all these networked applications, a common challenge is the provision of a persistent service, i.e., a service capable of consistently maintaining a high level of performance, facing possible failures. To address these service resilient challenges, we propose APRON, an edge solution for distributed and adaptive task planning management in a network of IoT devices, e.g., drones. Exploiting Jackson's network model, our architecture applies a novel planning strategy to better support control and monitoring operations while the states of the network evolve. To demonstrate the functionalities of our architecture, we also implemented a deep-learning based audio-recognition application using the APRON NorthBound interface, to detect human voices in challenged networks. The application's logic uses Transfer Learning to improve the audio classification accuracy and the runtime of the UAV-based rescue operations

Agents and Robots for Reliable Engineered Autonomy

This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

MRsensing: environmental monitoring and context recognition with cooperative mobile robots in catastrophic incidents

Dissertação de Mestrado em Engenharia Electrotécnica e de Computadores, apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraMulti-sensor information fusion theory concerns the environmental perception activities to combine data from multiple sensory resources. Humans, as any other animals, gather information from the environment around them using different biological sensors. Combining them allows structuring the decisions and actions when interacting with the environment. Under disaster conditions, effective mult-robot information sensor fusion can yield a better situation awareness to support the collective decision-making. Mobile robots can gather information from the environment by combining data from different sensors as a way to organize decisions and augment human perception. The is especially useful to retrieve contextual environmental information in catastrophic incidents where human perception may be limited (e.g., lack of visibility). To that end, this work proposes a specific configuration of sensors assembled in a mobile robot, which can be used as a proof of concept to measure important environmental variables in an urban search and rescue (USAR) mission, such as toxic gas density, temperature gradient and smoke particles density. This data is processed through a support vector machine classifier with the purpose of detecting relevant contexts in the course of the mission. The outcome provided by the experiments conducted with TraxBot and Pioneer-3DX robots under the Robot Operating System framework opens the door for new multi-robot applications on USAR scenarios. This work was developed within the CHOPIN research project1 which aims at exploiting the cooperation between human and robotic teams in catastrophic accidents.O tema da fusão sensorial abrange a perceção ambiental para combinar dados de vários recursos naturais. Os seres humanos, como todos os outros animais, recolhem informações do seu redor, utilizando diferentes sensores biológicos. Combinando-se informação dos diferentes sensores é possível estruturar decisões e ações ao interagir com o meio ambiente. Sob condições de desastres, a fusão sensorial de informação eficaz proveniente de múltiplos robôs pode levar a um melhor reconhecimento da situação para a tomada de decisão coletiva. Os robôs móveis podem extrair informações do ambiente através da combinação de dados de diferentes sensores, como forma de organizar as decisões e aumentar a perceção humana. Isto é especialmente útil para obter informações de contexto ambientais em cenários de catástrofe, onde a perceção humana pode ser limitada (por exemplo, a falta de visibilidade). Para este fim, este trabalho propõe uma configuração específica de sensores aplicados num robô móvel, que pode ser usado como prova de conceito para medir variáveis ambientais importantes em missões de busca e salvamento urbano (USAR), tais como a densidade do gás tóxico, gradiente de temperatura e densidade de partículas de fumo. Esta informação é processada através de uma máquina de vetores de suporte com a finalidade de classificar contextos relevantes no decorrer da missão. O resultado fornecido pelas experiências realizadas com os robôs TraxBot e Pioneer 3DX usando a arquitetura Robot Operating System abre a porta para novas aplicações com múltiplos robôs em cenários USAR

Learning and Using Context on a Humanoid Robot Using Latent Dirichlet Allocation

2014 Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob), Genoa, Italy, 13-16 October 2014In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts. We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning.Scientific and Technological Research Council of Turkey (TUBiTAK