Object transportation by a human and a mobile manipulator : a dynamical systems approach

In this paper we address the problem of humanrobot joint transportation of large payloads. The human brings to the task knowledge on the goal destination and global path planning. The robot has no prior knowledge of the environment and must autonomously help the human, while simultaneously avoiding static and/or dynamic obstacles that it encounters. For this purpose a dynamic control architecture, formalized as a coupled system of non-linear differential equations, is designed to control the behavior of the mobile manipulator in close loop with the acquired sensorial information. Verbal communication is integrated that allows the robot to communicate its limitations. Results show the robot’s ability to generate stable, smooth and robust behavior in unstructured and dynamic environments. Furthermore, the robot is able to explain the difficulties it encounters and thus contribute to success of the task and to enhance the human-robot physical interaction.FP6-IST2-EU-project JAST (project no 003747)Portuguese Science and Technology Foundation (FCT) and FEDER project COOPDYN (POSI/SRI/38081/2001)

Autonomous robot systems and competitions: proceedings of the 12th International Conference

This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories, methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions. All accepted contributions are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Transport coopératif d'un objet par deux robots humanoïdes dans un environnement encombré

Il y a présentement de la demande dans plusieurs milieux cherchant à utiliser des robots afin d'accomplir des tâches complexes, par exemple l'industrie de la construction désire des travailleurs pouvant travailler 24/7 ou encore effectuer des operation de sauvetage dans des zones compromises et dangereuses pour l'humain. Dans ces situations, il devient très important de pouvoir transporter des charges dans des environnements encombrés. Bien que ces dernières années il y a eu quelques études destinées à la navigation de robots dans ce type d'environnements, seulement quelques-unes d'entre elles ont abordé le problème de robots pouvant naviguer en déplaçant un objet volumineux ou lourd. Ceci est particulièrement utile pour transporter des charges ayant de poids et de formes variables, sans avoir à modifier physiquement le robot. Un robot humanoïde est une des plateformes disponibles afin d'effectuer efficacement ce type de transport. Celui-ci a, entre autres, l'avantage d'avoir des bras et ils peuvent donc les utiliser afin de manipuler précisément les objets à transporter. Dans ce mémoire de maîtrise, deux différentes techniques sont présentées. Dans la première partie, nous présentons un système inspiré par l'utilisation répandue de chariots de fortune par les humains. Celle-ci répond au problème d'un robot humanoïde naviguant dans un environnement encombré tout en déplaçant une charge lourde qui se trouve sur un chariot de fortune. Nous présentons un système de navigation complet, de la construction incrémentale d'une carte de l'environnement et du calcul des trajectoires sans collision à la commande pour exécuter ces trajectoires. Les principaux points présentés sont : 1) le contrôle de tout le corps permettant au robot humanoïde d'utiliser ses mains et ses bras pour contrôler les mouvements du système à chariot (par exemple, lors de virages serrés) ; 2) une approche sans capteur pour automatiquement sélectionner le jeu approprié de primitives en fonction du poids de la charge ; 3) un algorithme de planification de mouvement qui génère une trajectoire sans collisions en utilisant le jeu de primitive approprié et la carte construite de l'environnement ; 4) une technique de filtrage efficace permettant d'ignorer le chariot et le poids situés dans le champ de vue du robot tout en améliorant les performances générales des algorithmes de SLAM (Simultaneous Localization and Mapping) défini ; et 5) un processus continu et cohérent d'odométrie formés en fusionnant les informations visuelles et celles de l'odométrie du robot. Finalement, nous présentons des expériences menées sur un robot Nao, équipé d'un capteur RGB-D monté sur sa tête, poussant un chariot avec différentes masses. Nos expériences montrent que la charge utile peut être significativement augmentée sans changer physiquement le robot, et donc qu'il est possible d'augmenter la capacité du robot humanoïde dans des situations réelles. Dans la seconde partie, nous abordons le problème de faire naviguer deux robots humanoïdes dans un environnement encombré tout en transportant un très grand objet qui ne peut tout simplement pas être déplacé par un seul robot. Dans cette partie, plusieurs algorithmes et concepts présentés dans la partie précédente sont réutilisés et modifiés afin de convenir à un système comportant deux robot humanoides. Entre autres, nous avons un algorithme de planification de mouvement multi-robots utilisant un espace d'états à faible dimension afin de trouver une trajectoire sans obstacle en utilisant la carte construite de l'environnement, ainsi qu'un contrôle en temps réel efficace de tout le corps pour contrôler les mouvements du système robot-objet-robot en boucle fermée. Aussi, plusieurs systèmes ont été ajoutés, tels que la synchronisation utilisant le décalage relatif des robots, la projection des robots sur la base de leur position des mains ainsi que l'erreur de rétroaction visuelle calculée à partir de la caméra frontale du robot. Encore une fois, nous présentons des expériences faites sur des robots Nao équipés de capteurs RGB-D montés sur leurs têtes, se déplaçant avec un objet tout en contournant d'obstacles. Nos expériences montrent qu'un objet de taille non négligeable peut être transporté sans changer physiquement le robot

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected Event

abstract: Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after an unexpected event. These biases thus further influence people’s trust in their partners, including machine partners. The increasing capabilities and complexity of machines allow them to work physically with humans. However, their improvements may interfere with the accuracy for people to calibrate trust in machines and their capabilities, which requires an understanding of attribution biases’ effect on human-machine coordination. Specifically, the current thesis explores how the development of trust in a partner is influenced by attribution biases and people’s assignment of blame for a negative outcome. This study can also suggest how a machine partner should be designed to react to environmental disturbances and report the appropriate level of information about external conditions.Dissertation/ThesisMasters Thesis Human Systems Engineering 201

Towards adaptive and autonomous humanoid robots: from vision to actions

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions

Estudo do movimento humano em tarefas de transporte cooperativo

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresA interação humano-robô tem uma enorme aplicabilidade na realização de tarefas cooperativas do nosso quotidiano. Um exemplo, é o transporte de objetos largos e compridos, que devido à dificuldade da tarefa, um humano, sozinho, não seria capaz de efetuar. No entanto, com a ajuda de um segundo agente, um robô por exemplo, a tarefa já podia então ser concluída. Para que o transporte seja bem-sucedido é necessário que o humano e o robô estejam em sintonia para que no caso de ser encontrada uma dificuldade de percurso (e.g. corredor apertado ou corredor sem saída), não haja conflito entre os intervenientes. Nesta dissertação, pretendemos desenvolver uma experiência com equipas humano-humano a efetuar o transporte de um objeto comprido até uma localização alvo passando por um corredor mais apertado onde a coordenação entre os participantes se torna imperativa. A estratégia utilizada designa-se de Leader-Follower, onde o Leader sabe a localização alvo escolhendo o caminho até lá e o Follower tem como objetivo ajudar o Leader a completar a tarefa se possível sem colisões ao longo do percurso. O Follower não tem qualquer informação acerca do alvo. Durante a experiência, os participantes irão transportar dois tipos de objetos (objetos com tamanhos diferentes). A ausência de comunicação entre os intervenientes, a falta de informação acerca do ambiente e a visibilidade reduzida tornam-se obstáculos a superar pelos participantes. Inicialmente é feita uma descrição detalhada da experiência, onde é apresentado o cenário onde esta foi desenvolvida, o sistema de motion tracking usado para capturar as trajetórias dos participantes, os objetos a serem transportados pelos participantes, os chapéus responsáveis por limitar a quantidade de informação ao dispor dos participantes, e por fim o procedimento adotado. Seguidamente é explicado detalhadamente o processamento aplicado aos dados até à serem exportados. Finalmente são apresentadas as variáveis calculadas que descrevem as trajetórias dos participantes e os métodos estatísticos usados para apresentar e relacionar os resultados. Os resultados obtidos demonstram que o Leader da tarefa tem em atenção as dificuldades do parceiro adotando trajetórias mais seguras ao longo da experiência. Mostram também a existência de um efeito de aprendizagem por parte do participante Leader à medida que a experiência é realizada. Em relação ao Follower, conclui-se que este segue a orientação do objeto, no entanto, é o responsável por corrigir a trajetória na eventualidade de uma colisão.The human-robot interaction has a huge applicability in carrying out cooperative tasks of everyday life. An example of those applications are the joint-carrying tasks like the transport of wide and long objects. Given the difficulty of the task a human, by himself, would not be able to perform it successfully. However, with the help of a second agent, like a robot, the task could then be concluded. Nonetheless, to successfully accomplish the task, both agents need to be coordinated in space and time so any route difficulty (tighter corridor, dead end, etc.) can be solved accordingly. In this dissertation, we intend to develop an experiment with human-human teams while a transport of a long object through a tighter corridor is developed. In this case scenario the coordination between the participants becomes imperative. The strategy adopted is called the Leader-Follower, where the Leader knows the target location and he is responsible to choose the path to get there. The Follower has to help the Leader to complete the task preferably without any collision. The Follower does not have any information about the target’s location. During the experiment, two types of objects will be carried by the participants (objects with different sizes). The absence of communication between the participants, the lack of information about the environment and the reduced visibility become obstacles to overcome by the participants. First, a detailed description of the experience is given, where the scenario and the motion tracking system used to capture the trajectories of the participants are presented. Afterwards the objects to be transported by the participants, the hats responsible to limit the amount of information to available for participants and the procedure adopted are revised. Finally, it is explained how the collected data was processed and exported to be analyzed and which characteristics of the participant’s trajectories were explored and how they were calculated. The results shown that the Leader takes into account the difficulties of the Follower adopting safer paths through the experience. They also shown that there is a learning effect by the participant Leader as the experiment is performed. Regarding the Follower, it is concluded that he follows the orientation of the object. However, he is responsible for adapting his trajectory in order to avoid collisions