Telerobot task planning and reasoning: Introduction to JPL artificial intelligence research

A view of the capabilities and areas of artificial intelligence research which are required for autonomous space telerobotics extending through the year 2000 is given. In the coming years, JPL will be conducting directed research to achieve these capabilities, as well as drawing heavily on collaborative efforts conducted with other research laboratories

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

LeaF: A Learning-based Fault Diagnostic System for Multi-Robot Teams

The failure-prone complex operating environment of a standard multi-robot application dictates some amount of fault-tolerance to be incorporated into every system. In fact, the quality of the incorporated fault-tolerance has a direct impact on the overall performance of the system. Despite the extensive work being done in the field of multi-robot systems, there does not exist a general methodology for fault diagnosis and recovery. The objective of this research, in part, is to provide an adaptive approach that enables the robot team to autonomously detect and compensate for the wide variety of faults that could be experienced. The key feature of the developed approach is its ability to learn useful information from encountered faults, unique or otherwise, towards a more robust system. As part of this research, we analyzed an existing multi-agent architecture, CMM – Causal Model Method – as a fault diagnostic solution for a sample multi-robot application. Based on the analysis, we claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors. However, the analysis also showed that the CMM method in its current form is incomplete as a turn-key solution. Due to the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Therefore, based on these preliminary studies, we designed an alternate approach, called LeaF: Learning based Fault diagnostic architecture for multi-robot teams. LeaF is an adaptive method that uses its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. LeaF combines the initial fault model with a case-based learning algorithm, LID – Lazy Induction of Descriptions — to allow robot team members to diagnose faults and to automatically update their causal models. The modified LID algorithm uses structural similarity between fault characteristics as a means of classifying previously un-encountered faults. Furthermore, the use of learning allows the system to identify and categorize unexpected faults, enable team members to learn from problems encountered by others, and make intelligent decisions regarding the environment. To evaluate LeaF, we implemented it in two challenging and dynamic physical multi-robot applications. The other significant contribution of the research is the development of metrics to measure the fault-tolerance, within the context of system performance, for a multi-robot system. In addition to developing these metrics, we also outline potential methods to better interpret the obtained measures towards truly understanding the capabilities of the implemented system. The developed metrics are designed to be application independent and can be used to evaluate and/or compare different fault-tolerance architectures like CMM and LeaF. To the best of our knowledge, this approach is the only one that attempts to capture the effect of intelligence, reasoning, or learning on the effective fault-tolerance of the system, rather than relying purely on traditional redundancy based measures. Finally, we show the utility of the designed metrics by applying them to the obtained physical robot experiments, measuring the effective fault-tolerance and system performance, and subsequently analyzing the calculated measures to help better understand the capabilities of LeaF

Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Fault Recovery in Swarm Robotics Systems using Learning Algorithms

When faults occur in swarm robotic systems they can have a detrimental effect on collective behaviours, to the point that failed individuals may jeopardise the swarm's ability to complete its task. Although fault tolerance is a desirable property of swarm robotic systems, fault recovery mechanisms have not yet been thoroughly explored. Individual robots may suffer a variety of faults, which will affect collective behaviours in different ways, therefore a recovery process is required that can cope with many different failure scenarios. In this thesis, we propose a novel approach for fault recovery in robot swarms that uses Reinforcement Learning and Self-Organising Maps to select the most appropriate recovery strategy for any given scenario. The learning process is evaluated in both centralised and distributed settings. Additionally, we experimentally evaluate the performance of this approach in comparison to random selection of fault recovery strategies, using simulated collective phototaxis, aggregation and foraging tasks as case studies. Our results show that this machine learning approach outperforms random selection, and allows swarm robotic systems to recover from faults that would otherwise prevent the swarm from completing its mission. This work builds upon existing research in fault detection and diagnosis in robot swarms, with the aim of creating a fully fault-tolerant swarm capable of long-term autonomy

Diagnosis of an EPS module

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e ComputadoresThis thesis addresses and contextualizes the problem of diagnostic of an Evolvable Production System (EPS). An EPS is a complex and lively entity composed of intelligent modules that interact through bio-inspired mechanisms, to ensure high system availability and seamless reconfiguration. The actual economic situation together with the increasing demand of high quality and low priced customized products imposed a shift in the production policies of enterprises. Shop floors have to become more agile and flexible to accommodate the new production paradigms. Rather than selling products enterprises are establishing a trend of offering services to explore business opportunities. The new production paradigms, potentiated by the advances in Information Technologies (IT), especially in web related standards and technologies as well as the progressive acceptance of the multi-agent systems (MAS) concept and related technologies, envision collections of modules whose individual and collective function adapts and evolves ensuring the fitness and adequacy of the shop floor in tackling profitable but volatile business opportunities. Despite the richness of the interactions and the effort set in modelling them, their potential to favour fault propagation and interference, in these complex environments, has been ignored from a diagnostic point of view. With the increase of distributed and autonomous components that interact in the execution of processes current diagnostic approaches will soon be insufficient. While current system dynamics are complex and to a certain extent unpredictable the adoption of the next generation of approaches and technologies comes at the cost of a yet increased complexity.Whereas most of the research in such distributed industrial systems is focused in the study and establishment of control structures, the problem of diagnosis has been left relatively unattended. There are however significant open challenges in the diagnosis of such modular systems including: understanding fault propagation and ensuring scalability and co-evolution. This work provides an implementation of a state-of-the-art agent-based interaction-oriented architecture compliant with the EPS paradigm that supports the introduction of a new developed diagnostic algorithm that has the ability to cope with the modern manufacturing paradigm challenges and to provide diagnostic analysis that explores the network dimension of multi-agent systems

A model-based approach for supporting flexible automation production systems and an agent-based implementaction

158 p.En esta Tesis Doctoral se plantea una arquitectura de gestión genérica y personalizable, capaz de asegurar el cumplimiento de los requisitos de calidad de servicio (QoS) de un sistema de control industrial. Esta arquitectura permite la modificación de los mecanismos de detección y recuperación de los requisitos de QoS en función de diversos tipos de ésta. Como prueba de concepto, la arquitectura de gestión ha sido implementada mediante un middleware basado en sistemas multi-agente. Este middleware proporciona una serie de agentes distribuidos, los cuales se encargan de la monitorización y recuperación de las QoS en caso de su perdida.La incorporación de los mecanismos de reconfiguración incrementa la complejidad de los sistemas de control. Con el fin de facilitar el diseño de estos sistemas, se ha presentado un framework basado en modelos que guía y facilita el diseño de los sistemas de control reconfigurables. Este framework proporciona una serie de herramientas basadas en modelos que permiten la generación automática del código de control del sistema, así como de los mecanismos de monitorización y reconfiguración de los agentes del middleware.La implementación de la arquitectura ha sido validada mediante una serie de escenarios basados en una célula de montaje real

COACHES Cooperative Autonomous Robots in Complex and Human Populated Environments

Public spaces in large cities are increasingly becoming complex and unwelcoming environments. Public spaces progressively become more hostile and unpleasant to use because of the overcrowding and complex information in signboards. It is in the interest of cities to make their public spaces easier to use, friendlier to visitors and safer to increasing elderly population and to citizens with disabilities. Meanwhile, we observe, in the last decade a tremendous progress in the development of robots in dynamic, complex and uncertain environments. The new challenge for the near future is to deploy a network of robots in public spaces to accomplish services that can help humans. Inspired by the aforementioned challenges, COACHES project addresses fundamental issues related to the design of a robust system of self-directed autonomous robots with high-level skills of environment modelling and scene understanding, distributed autonomous decision-making, short-term interacting with humans and robust and safe navigation in overcrowding spaces. To this end, COACHES will provide an integrated solution to new challenges on: (1) a knowledge-based representation of the environment, (2) human activities and needs estimation using Markov and Bayesian techniques, (3) distributed decision-making under uncertainty to collectively plan activities of assistance, guidance and delivery tasks using Decentralized Partially Observable Markov Decision Processes with efficient algorithms to improve their scalability and (4) a multi-modal and short-term human-robot interaction to exchange information and requests. COACHES project will provide a modular architecture to be integrated in real robots. We deploy COACHES at Caen city in a mall called “Rive de l’orne”. COACHES is a cooperative system consisting of ?xed cameras and the mobile robots. The ?xed cameras can do object detection, tracking and abnormal events detection (objects or behaviour). The robots combine these information with the ones perceived via their own sensor, to provide information through its multi-modal interface, guide people to their destinations, show tramway stations and transport goods for elderly people, etc.... The COACHES robots will use different modalities (speech and displayed information) to interact with the mall visitors, shopkeepers and mall managers. The project has enlisted an important an end-user (Caen la mer) providing the scenarios where the COACHES robots and systems will be deployed, and gather together universities with complementary competences from cognitive systems (SU), robust image/video processing (VUB, UNICAEN), and semantic scene analysis and understanding (VUB), Collective decision-making using decentralized partially observable Markov Decision Processes and multi-agent planning (UNICAEN, Sapienza), multi-modal and short-term human-robot interaction (Sapienza, UNICAEN