Maximum Likelihood Uncertainty Estimation: Robustness to Outliers

We benchmark the robustness of maximum likelihood based uncertainty estimation methods to outliers in training data for regression tasks. Outliers or noisy labels in training data results in degraded performances as well as incorrect estimation of uncertainty. We propose the use of a heavy-tailed distribution (Laplace distribution) to improve the robustness to outliers. This property is evaluated using standard regression benchmarks and on a high-dimensional regression task of monocular depth estimation, both containing outliers. In particular, heavy-tailed distribution based maximum likelihood provides better uncertainty estimates, better separation in uncertainty for out-of-distribution data, as well as better detection of adversarial attacks in the presence of outliers

La cadena logística y posicionamiento de servicios de la agencia de aduanas WORLD INTERNATIONAL S.A.C. Callao, 2018

Esta investigación busca delimitar la influencia de la Cadena logística sobre el posicionamiento de servicios, para lo cual se procedió con la indagación en fuentes bibliográficas para la obtención de información científica. La población está conformada por los colaboradores de WORLD INTERNATIONAL ADUANAS S.A.C. en el Callaol, la muestra está conformada por 30 colaboradores. Como instrumento de medición de utilizó un cuestionario constituido por 20 preguntas aplicando la escala de Likert, posteriormente las respuestas fueron procesadas, analizadas con el programa estadístico SPSS, midiéndose la confiabilidad del cuestionario a través del alfa de cronbach. Además, se usó la prueba de correlación de Pearson para medir la correlación e influencia entre las variables y finalmente se interpretaron los gráficos estadísticos elaborados para cada pregunta obteniéndose resultados significativos en donde se comprueba la existencia de una gran influencia de la Planeación estratégica de Marketing sobre la captación de clientes

Implementation and validation of an event-based real-time nonlinear model predictive control framework with ROS interface for single and multi-robot systems.

This paper presents the implementation and experimental validation of a central control framework. The presented framework addresses the need for a controller, which provides high performance combined with a low-computational load while being on-line adaptable to changes in the control scenario. Examples for such scenarios are cooperative control, task-based control and fault-tolerant control, where the system's topology, dynamics, objectives and constraints are changing. The framework combines a fast Nonlinear Model Predictive Control (NMPC), a communication interface with the Robot Operating System (ROS) as well as a modularization that allows an event-based change of the NMPC scenario. To experimentally validate performance and event-based adaptability of the framework, this paper is using a cooperative control scenario of Unmanned Aerial Vehicles (UAVs)

Deep Reinforcement Learning based Continuous Control for Multicopter Systems

In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable hovering task in a continuous action state environment. We present a framework based on OpenAI GYM, Gazebo and RotorS MAV simulator, utilized for successfully training different agents to perform various tasks. The deep reinforcement learning method used for the training is model-free, on-policy, actor-critic based algorithm called Trust Region Policy Optimization (TRPO). Two neural networks have been used as a nonlinear function approximators. Our experiments showed that such learning approach achieves successful results, and facilitates the process of controller design

GPS-aided Visual Wheel Odometry

This paper introduces a novel GPS-aided visual-wheel odometry (GPS-VWO) for ground robots. The state estimation algorithm tightly fuses visual, wheeled encoder and GPS measurements in the way of Multi-State Constraint Kalman Filter (MSCKF). To avoid accumulating calibration errors over time, the proposed algorithm calculates the extrinsic rotation parameter between the GPS global coordinate frame and the VWO reference frame online as part of the estimation process. The convergence of this extrinsic parameter is guaranteed by the observability analysis and verified by using real-world visual and wheel encoder measurements as well as simulated GPS measurements. Moreover, a novel theoretical finding is presented that the variance of unobservable state could converge to zero for specific Kalman filter system. We evaluate the proposed system extensively in large-scale urban driving scenarios. The results demonstrate that better accuracy than GPS is achieved through the fusion of GPS and VWO. The comparison between extrinsic parameter calibration and non-calibration shows significant improvement in localization accuracy thanks to the online calibration.Comment: Accepted by IEEE ITSC 202

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints: a workflow to create sensor constraint based potential functions for the control of cooperative localization scenarios with mobile robots.

The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver

Trajectory Optimization and Following for a Three Degrees of Freedom Overactuated Floating Platform

Space robotics applications, such as Active Space Debris Removal (ASDR), require representative testing before launch. A commonly used approach to emulate the microgravity environment in space is air-bearing based platforms on flat-floors, such as the European Space Agency's Orbital Robotics and GNC Lab (ORGL). This work proposes a control architecture for a floating platform at the ORGL, equipped with eight solenoid-valve-based thrusters and one reaction wheel. The control architecture consists of two main components: a trajectory planner that finds optimal trajectories connecting two states and a trajectory follower that follows any physically feasible trajectory. The controller is first evaluated within an introduced simulation, achieving a 100 % success rate at finding and following trajectories to the origin within a Monte-Carlo test. Individual trajectories are also successfully followed by the physical system. In this work, we showcase the ability of the controller to reject disturbances and follow a straight-line trajectory within tens of centimeters.Comment: Accepted to IROS2022, code at https://gitlab.com/anton.bredenbeck/ff-trajectorie

Estudio de pre factibilidad para la instalación de una planta procesadora de aceite de palta hass (Persea americana)

El presente proyecto de investigación tiene como finalidad comprobar la rentabilidad de la implementación de una planta procesadora de Aceite de Palta Hass. Hoy en día el mercado de productos orgánicos ha crecido notablemente, principalmente por el interés de las personas en el cuidado de su alimentación para mantener un cuerpo saludable y evitar futuras enfermedades. Como bien se sabe, la palta contiene alto nivel nutricional, ya que eleva los niveles de colesterol bueno (HDL) y reduce el colesterol malo (LDL). Este es el motivo por el cual, se considera la comercialización de este producto como una oportunidad de negocio exitosa. El trabajo de investigación consta de 9 capítulos. En el primero se presentan los aspectos generales del proyecto, es decir, los objetivos generales y específicos, la justificación del tema, la hipótesis sobre la viabilidad del proyecto y el marco conceptual para el desarrollo de la investigación.The purpose of this investigation Project is to verify the profitability of the implementation of a hass avocado oil processing plant. Nowadays organic products has grown remarkably, mainly for the interest of the people in the care of their food to have a healthy body and avoid future diseases. As is well known, the avocado has a high nutritional level, because it raises the levels of good cholesterol (HDL) and reduces the bad cholesterol (LDL). This is the reason why, the commercialization of this product is considered as a successful business opportunity. The research work consists of 9 chapters, the first one presents the general aspects of the project, general and specific objectives, the justification of the topic, the hypothesis about the viability of the project and the conceptual framework for the development of the research