Advanced mechanisms for robotics

An overview of applied research and development at NASA-Goddard (GSFC) on mechanisms and the collision avoidance skin for robots is presented. First the work on robot end effectors is outlined, followed by a brief discussion on robot-friendly payload latching mechanisms and compliant joints. This, in turn, is followed by the collision avoidance/management skin and the GSFC research on magnetostrictive direct drive motors. Finally, a new project, the artificial muscle, is introduced. Each of the devices is described in sufficient detail to permit a basic understanding of its purpose, fundamental principles of operation, and capabilities. In addition, the development status of each is reported along with descriptions of breadboards and prototypes and their test results. In each case, the implications of the research for commercialization is discussed. The chronology of the presentation will give a clear idea of both the evolution of the R&D in recent years and its likely direction in the future

Human aware robot navigation

Abstract. Human aware robot navigation refers to the navigation of a robot in an environment shared with humans in such a way that the humans should feel comfortable, and natural with the presence of the robot. On top of that, the robot navigation should comply with the social norms of the environment. The robot can interact with humans in the environment, such as avoiding them, approaching them, or following them. In this thesis, we specifically focus on the approach behavior of the robot, keeping the other use cases still in mind. Studying and analyzing how humans move around other humans gives us the idea about the kind of navigation behaviors that we expect the robots to exhibit. Most of the previous research does not focus much on understanding such behavioral aspects while approaching people. On top of that, a straightforward mathematical modeling of complex human behaviors is very difficult. So, in this thesis, we proposed an Inverse Reinforcement Learning (IRL) framework based on Guided Cost Learning (GCL) to learn these behaviors from demonstration. After analyzing the CongreG8 dataset, we found that the incoming human tends to make an O-space (circle) with the rest of the group. Also, the approaching velocity slows down when the approaching human gets closer to the group. We utilized these findings in our framework that can learn the optimal reward and policy from the example demonstrations and imitate similar human motion

Zonas sociales cognitivas para mejorar la evasión de peatones con robots móviles

Los comportamientos sociales son esenciales para mejorar la aceptación social de un robot en ambientes compartidos con humanos. Uno de las cualidades más importantes es sin duda el espacio social. Este mecanismo humano actúa como un campo repulsivo para garantizar interacciones confortables. Su modelado ha sido ampliamente estudiado en robótica social, sin embargo su inferencia experimental ha sido apenas mencionada. De esta manera, este trabajo propone un novedoso algoritmo para inferir las dimensiones de una zona social elíptica a partir de una nube de puntos alrededor del robot. El enfoque consiste en identificar cómo los humanos evitan al robot durante una evasión en un ambiente compartido, y posteriormente usar esta experiencia para representar obstáculos humanos como campos elípticos potenciales con las dimensiones previamente identificadas. Para ésto, el algoritmo empieza con una primera etapa de aprendizaje donde el robot navega sin evadir a los humanos, i.e. los humanos estan a cargo de evadir al robot durante el desenvolvimiento de sus tareas. Durante este periodo, el robot genera una nube de puntos de mediciones laser 2D desde su marco de referencia para definir las zonas de no-inferencia humana alrededor de sí mismo, pero priorizando sus cercanías. Posteriormente, la zona social que ha sido inferida se incorpora a un control de movimiento basado en espacios nulos (NSB) para un robot móvil no holonómico, el cual se diseña para seguir trayectorias y evitar colisiones con peatones. Finalmente, el rendimiento del algoritmo de aprendizaje y el control de movimiento es verificado experimentalmente.Social behaviors are crucial to improve the acceptance of a robot in human-shared environments. One of the most important social cues is undoubtedly the social space. This human mechanism acts like a repulsive field to guarantee comfortable interactions. Its modeling has been widely studied in social robotics, but its experimental inference has been weakly mentioned. Thereby, this paper proposes a novel algorithm to infer the dimensions of an elliptical social zone from a points-cloud around the robot. The approach consists of identifying how the humans avoid a robot during navigation in shared scenarios, and later use this experience to represent humans obstacles like elliptical potential fields with the previously identified dimensions. Thus, the algorithm starts with a first-learning stage where the robot navigates without avoiding humans, i.e. the humans are in charge of avoiding the robots while developing their tasks. During this period, the robot generates a points-cloud with 2D laser measures from its own framework to define the human-presence zones around itself but prioritizing its closest surroundings. Later, the inferred social zone is incorporated to a null-space-based (NSB) control for a non-holonomic mobile robot, which consists of both trajectory tracking and pedestrian collision avoidance. Finally, the performance of the learning algorithm and the motion control is verified through experimentation.Fil: Daniel Herrera. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Giménez, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Monllor, Matias Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Roberti, Flavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Robot Control for Task Performance and Enhanced Safety under Impact

A control law combining motion performance quality and low stiffness reaction to unintended contacts is proposed in this work. It achieves prescribed performance evolution of the position error under disturbances up to a level related to model uncertainties and responds compliantly and with low stiffness to significant disturbances arising from impact forces. The controller employs a velocity reference signal in a model-based control law utilizing a non-linear time-dependent term, which embeds prescribed performance specifications and vanishes in case of significant disturbances. Simulation results with a three degrees of freedom (DOF) robot illustrate the motion performance and self-regulation of the output stiffness achieved by this controller under an external force, and highlights its advantages with respect to constant and switched impedance schemes. Experiments with a KUKA LWR4+ demonstrate its performance under impact with a human while following a desired trajectory

Learning to represent surroundings, anticipate motion and take informed actions in unstructured environments

Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly