Adaptive and intelligent navigation of autonomous planetary rovers - A survey

The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors

Active Object Classification from 3D Range Data with Mobile Robots

This thesis addresses the problem of how to improve the acquisition of 3D range data with a mobile robot for the task of object classification. Establishing the identities of objects in unknown environments is fundamental for robotic systems and helps enable many abilities such as grasping, manipulation, or semantic mapping. Objects are recognised by data obtained from sensor observations, however, data is highly dependent on viewpoint; the variation in position and orientation of the sensor relative to an object can result in large variation in the perception quality. Additionally, cluttered environments present a further challenge because key data may be missing. These issues are not always solved by traditional passive systems where data are collected from a fixed navigation process then fed into a perception pipeline. This thesis considers an active approach to data collection by deciding where is most appropriate to make observations for the perception task. The core contributions of this thesis are a non-myopic planning strategy to collect data efficiently under resource constraints, and supporting viewpoint prediction and evaluation methods for object classification. Our approach to planning uses Monte Carlo methods coupled with a classifier based on non-parametric Bayesian regression. We present a novel anytime and non-myopic planning algorithm, Monte Carlo active perception, that extends Monte Carlo tree search to partially observable environments and the active perception problem. This is combined with a particle-based estimation process and a learned observation likelihood model that uses Gaussian process regression. To support planning, we present 3D point cloud prediction algorithms and utility functions that measure the quality of viewpoints by their discriminatory ability and effectiveness under occlusion. The utility of viewpoints is quantified by information-theoretic metrics, such as mutual information, and an alternative utility function that exploits learned data is developed for special cases. The algorithms in this thesis are demonstrated in a variety of scenarios. We extensively test our online planning and classification methods in simulation as well as with indoor and outdoor datasets. Furthermore, we perform hardware experiments with different mobile platforms equipped with different types of sensors. Most significantly, our hardware experiments with an outdoor robot are to our knowledge the first demonstrations of online active perception in a real outdoor environment. Active perception has broad significance in many applications. This thesis emphasises the advantages of an active approach to object classification and presents its assimilation with a wide range of robotic systems, sensors, and perception algorithms. By demonstration of performance enhancements and diversity, our hope is that the concept of considering perception and planning in an integrated manner will be of benefit in improving current systems that rely on passive data collection

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

Spatio-Temporal Terrain Classification for Mapping and Robot Localization

Detection and classification of the surrounding terrain is a fundamental ability of a mobile robot in outdoor navigation to enable safe and efficient path planning. Our robot is equipped with a 3D LiDAR and a color camera, since these sensors complement each other very well. The terrain in front of the robot is divided into a grid, and each grid cell is classified individually using the sensor measurements. A new method for 3D LiDAR-based terrain classification with easy-to-compute and yet discriminative 3D features based on intensity and roughness histograms is presented. To exploit the fact that terrain appears in contiguous areas, spatial dependencies between the individual cells of the terrain grid are taken into account by modeling the grid as a Conditional random field. As the robot moves, we constantly update a terrain map with the current classification result. In this way, we are not only able to exploit temporal dependencies, but we are building whole terrain maps of the environment. Finally, we show how to use these maps for a semantic localization of the robot

Aplicaciones en Economía del Aprendizaje Automático

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 06-05-2022This Thesis examines problems in economics from a Machine Learning perspective. Emphasisis given on the interpretability of Machine Learning algorithms as opposed to blackbox predictions models. Chapter 1 provides an overview of the terminology and Machine Learning methods used throughout this Thesis. This chapter aims to build a roadmap from simple decision tree models to more advanced ensemble boosted algorithms. Other Machine Learning models are also explained. A discussion of the advances in Machine Learning in economics is also provided along with some of the pitfalls that Machine Learning faces. Moreover, an example of how Shapley values from coalition game theory are used to help infer inference from the Machine Learning models' predictions. Chapter 2 analyses the problem of bankruptcy prediction in the Spanish economy and how Machine Learning, not only provides more predictive accuracy, but can also provide adierent interpretation of the results that traditional econometric models cannot. Several financial ratios are constructed and passed to a series of Machine Learning algorithms. Case studies are provided which may aid in better decision-making from financial institutions. A section containing supplementary material based on further analysis is also provided...Este Tesis examina problemas en economía desde la perspectiva de Aprendizaje Mecánico. Se hace hincapié en la interpretabilidad de los algoritmos de Aprendizaje Mecánico en lugar de modelos de predicción de black-box. Capítulo 1 Proporciona el resumen de la terminología y los métodos de Aprendizaje Mecánico utilizados a lo largo de esta tesis. El objetivo de este capítulo es construir la trayectoria desde un simple árbol de decisión hasta algoritmos impulsados por conjuntos más avanzados. También se explican otros modelos de Machine Learning. Asimismo, se proporciona una discusión de los avances en el Aprendizaje Mecánico en economía junto con algunos de los escollos que enfrenta el aprendizaje automático. Además, un ejemplo sobre cómo se utilizan los valores de Shapley de coalición de teoría de juegos y muestran cómo se puede tomar inferencia de los modelos de predicción. Capítulo 2 Analiza el problema de la predicción de quiebra en la economía española y cómo Aprendizaje Mecánico, no sólo proporciona una mayor precisión predictiva, sino que también puede proporcionar una interpretación diferente de los resultados en la que los modelos econométricos tradicionales no pueden. Se construyen una serie de ratios financieros y se pasan a una serie de algoritmos de Aprendizaje Mecánico. Se proporcionan estudios de casos que pueden ayudar a mejorar la toma de decisiones por parte de las instituciones financieras. También se proporciona una sección que contiene material complementario basado en un análisis más detallado...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance

In this thesis, we tackle the problem of extending neural network navigation algorithms for various types of mobile robots and 2-dimensional range sensors. We propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and mobile robot platforms. Moreover, this method allows the neural networks to be trained using only one type of 2-dimensional range sensor, which contributes positively to reducing the time required for training the networks. Experimental results carried out in simulation environments demonstrate the effectiveness of our approach in mobile robot navigation for different kinds of robots and sensors. Therefore, the successful implementation of our method provides a solution to apply mobile robot navigation algorithms to various robot platforms

Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic