From locomotion to cognition: Bridging the gap between reactive and cognitive behavior in a quadruped robot

The cognitivistic paradigm, which states that cognition is a result of computation with symbols that represent the world, has been challenged by many. The opponents have primarily criticized the detachment from direct interaction with the world and pointed to some fundamental problems (for instance the symbol grounding problem). Instead, they emphasized the constitutive role of embodied interaction with the environment. This has motivated the advancement of synthetic methodologies: the phenomenon of interest (cognition) can be studied by building and investigating whole brain-body-environment systems. Our work is centered around a compliant quadruped robot equipped with a multimodal sensory set. In a series of case studies, we investigate the structure of the sensorimotor space that the application of different actions in different environments by the robot brings about. Then, we study how the agent can autonomously abstract the regularities that are induced by the different conditions and use them to improve its behavior. The agent is engaged in path integration, terrain discrimination and gait adaptation, and moving target following tasks. The nature of the tasks forces the robot to leave the ``here-and-now'' time scale of simple reactive stimulus-response behaviors and to learn from its experience, thus creating a ``minimally cognitive'' setting. Solutions to these problems are developed by the agent in a bottom-up fashion. The complete scenarios are then used to illuminate the concepts that are believed to lie at the basis of cognition: sensorimotor contingencies, body schema, and forward internal models. Finally, we discuss how the presented solutions are relevant for applications in robotics, in particular in the area of autonomous model acquisition and adaptation, and, in mobile robots, in dead reckoning and traversability detection

A Framework for Mobile Augmented Reality in Urban Maintenance

Mobile handheld devices such as smartphones have become increasingly powerful in modern times. Because of this, there has been a surge in 3D graphics-heavy mobile applications that aim to provide immersive experiences. An example of this phenomenon would be Augmented Reality (AR) applications, which have been increasingly popular and offer a wide array of use-cases. The ability to merge the real world with the virtual world seamlessly using the built-in camera of the smartphone brings a whole new world of possibilities, which makes it interesting to explore how such a technology could be used to solve real-world problems. This dissertation focuses on applying this technology in the field of urban maintenance. To do so, a mobile AR application was developed, designed to be used by urban maintenance workers as a field-assistance tool. Using any standard smartphone camera, the developed system can accurately detect any equipment and augment it with relevant information and step-by-step instructions on how to do any required maintenance jobs. Alongside this mobile application, a desktop application was also developed with the purposes of creating and authoring the data and augmentations that should be displayed during a given job, called Archer. Lastly, this dissertation proposes a novel approach to automatically detect and minimize the amount of points (checkpoints) at which the application will ask the user to perform a new equipment recognition, which are useful in order to maintain tracking stability as the user modifies the real-world object during the course of the job. The experiments and user tests conducted during the final stages of this dissertation demonstrate the accuracy and practicality of the developed systems, proving that they can effectively be used to greatly improve the workflow of urban maintenance workers.Dispositivos móveis tais como os smartphones têm-se tornado cada vez mais poderosos nos tempos modernos. Como tal, tem havido um grande aumento na quantidade de aplicações móveis 3D com o intuito de fornecer experiências imersivas. Um exemplo desse fenómeno são as aplicações de Realidade Aumentada (RA), as quais se têm tornado cada vez mais populares, oferecendo um vasto leque de casos de uso. A habilidade de fundir o mundo real com o mundo virtual através da câmara de um smartphone traz todo um novo mundo de possibilidades, o que torna interessante a exploração de como esta tecnologia pode ser usada para resolver problemas no mundo real. Esta dissertação foca-se na aplicação desta tecnologia na área da manutenção urbana. Nesse sentido, foi desenvolvida uma aplicação móvel de RA projetada para ser usada como uma ferramenta de assistência em campo por trabalhadores de manutenção urbana. Usando qualquer câmara de smartphone, este sistema consegue detetar qualquer equipamento de forma precisa e aumentá-lo digitalmente com informação relevante e instruções passo-a-passo de como fazer qualquer trabalho de manutenção. Juntamente com esta aplicação móvel, também foi desenvolvida uma aplicação para desktop — chamada Archer — com o intuito de criar e validar os dados e os objetos digitais que serão apresentados na aplicação mobile durante o curso de um trabalho de manutenção. Por fim, esta dissertação apresenta uma nova solução para a deteção e minimização automática dos pontos (checkpoints) em que a aplicação móvel deverá pedir ao utilizador para efetuar um novo reconhecimento do equipamento, os quais são úteis para manter um tracking fiável e estável à medida que o utilizador vai modificando o equipamento durante um trabalho. As experiências e testes com utilizadores efetuados na fase final desta dissertação demonstram a precisão e praticidade dos sistemas desenvolvidos, provando que estes podem efetivamente ser usados para melhorar o workflow dos trabalhadores de manutenção urbana

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

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

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

Informational Framework for Minimalistic Visual Odometry on Outdoor Robot

International audienceIn an unknown environment, assessing the robot trajectory in real time is one of the key issues for a successful robotic mission. In such environment, the absolute measurements like the GPS data may be unavailable. Moreover, estimating the position using only proprioceptive sensors like encoders and Inertial Measurements Units (IMU) will generate errors that increase over time. This paper presents a multi-sensor fusion approach between IMU and ground Optical Flow (OF) used to estimate the position of a mobile robot while ensuring high integrity localization. The data fusion in done through the informational form of the Kalman Filter namely Information Filter (IF). A Fault Detection and Exclusion (FDE) step is added in order to exclude the erroneous measurements from the fusion procedure by making it fault tolerant and to ensure a high localization performance. The approach is based on the use of the IF for the state estimation and tools from the information theory for the FDE. Our proposed approach evaluates the quality of a measurement based on the amount of information it provides using informational metrics like the Kullback-Leibler divergence. The approach is validated on data obtained from experiments performed in outdoor environments in various conditions including high-dynamic-range lighting and different ground textures