Simulating and Training Autonomous Rover Navigation in Unity Engine Using Local Sensor Data

Autonomous navigation is essential to remotely operating mobile vehicles on Mars, as communication takes up to 20 minutes to travel between the Earth and Mars. Several autonomous navigation methods have been implemented in Mars rovers and other mobile robots, such as odometry or simultaneous localization and mapping (SLAM) until the past few years when deep reinforcement learning (DRL) emerged as a viable alternative. In this thesis, a simulation model for end-to-end DRL Mars rover autonomous navigation training was created using Unity Engine, using local inputs such as GNSS, LiDAR, and gyro. This model was then trained in navigation in a flat environment using the proximal policy optimization (PPO) algorithm. The results of the training and future work are discussed

Characterizing and evaluating autonomous controllers

Premio Extraordinario de Doctorado de la UAH en el año académico 2016-2017La autonomía en robótica por medio de técnicas de Inteligencia Artificial, particularmente mediante el empleo sistemas de Planning & Scheduling (P&S), presenta un amplio campo de investigación con gran interés en aplicaciones como la robótica de exploración para entornos hostiles o difícilmente accesibles para los humanos. Sin embargo, las pruebas experimentales realizadas en los artículos de divulgación científica sobre controladores autónomos generalmente no están correctamente realizadas, ya que se carece de una metodología de estudio común. En este sentido se hace complicado comparar los nuevos sistemas con los trabajos previos, práctica habitual en otras disciplinas. Por ello, en esta tesis se propone un entorno de trabajo llamado On-Ground Autonomy Test Environment (OGATE) para permitir la evaluación de controladores autónomos. Este desarrollo consta de una metodología para estructurar la fase experimental, así como de un conjunto de métricas independientes tanto del dominio como del campo de aplicación del sistema robótico. La unión de estos elementos, mediante un software que automatiza el proceso experimental, permite obtener evaluaciones reproducibles y objetivas sobre los controladores autónomos bajo estudio. Para demostrar la efectividad del entorno de trabajo, se han utilizado dos controladores autónomos basados en diferentes paradigmas para P&S. Primero se ha utilizado el Goal Oriented Autonomous Controller (GOAC), desarrollado bajo contrato de la Agencia Espacial Europea. Segundo, durante esta tesis se ha implementado la Model-Based Architecture (MoBAr). MoBAr está diseñado con el objetivo de probar diferentes planificadores basados en el Planning Domain Definition Language (PDDL) para conseguir autonomía a bordo. En este sentido, en la tesis también se introduce un nuevo planificador llamado Unified Path Planning and Task Planning Architecture (UP2TA). Dicho sistema integra un planificador general basado en PDDL y algoritmos de planificación de rutas con el objetivo de generar planes más seguros y eficientes para robots de exploración. Referente a la planificación de rutas, en la tesis se incluye la definición de dos nuevos algoritmos enfocados en la movilidad de los robots de exploración: S-Theta* y 3D Accurate Navigation Algorithm (3Dana). S-Theta* permite obtener rutas con un menor número de cambios de dirección que algoritmos previos, mientras que 3Dana genera rutas más seguras y restringidas en función de la pendiente del entorno, empleando para ello Modelos Digitales de Terreno (MDT) y mapas de costes trasversales. Partiendo de GOAC y MoBAr, se ha empleado OGATE para evaluar ambos controladores, siendo posible caracterizar aspectos relevantes de la integración entre Planning & Execution (P&E) difícilmente accesibles mediante otros enfoques. Además, los resultados obtenidos son objetivos y reproducibles, permitiendo realizar comparaciones entre controladores autónomos con diferentes tecnologías y/o paradigmas de P&S

Engineering Challenges Ahead for Robot Teamwork in Dynamic Environments

Gefördert durch den Publikationsfonds der Universität Kasse

ARQUITECTURA DE CONTROL CONDUCTUAL PARA AGENTES INTELIGENTES (ARCHITECTURE OF BEHAVIORAL CONTROL FOR INTELLIGENT AGENTS)

En este trabajo se simula, por medio del lenguaje de programación NetLogo, el comportamiento adaptativo de un agente inteligente ante su medio ambiente. El comportamiento está regido por una arquitectura de control conductual de inspiración biológica que se implementa a partir de máquinas de estado. Con este tipo de arquitectura, se aborda la problemática de que el agente elija la respuesta conductual más apropiada en función de las circunstancias de su entorno y de la estimulación recibida. Se reporta y compara el funcionamiento del agente a partir de dos experimentos que utilizan 5 escenarios y 4 controladores. Las simulaciones de este comportamiento inteligente se pueden implementar en robots móviles autónomos, en agentes asistentes o tutores, o en aquellos agentes que buscan y recuperan información en bases de datos o en Internet (softbots).This work simulates, through the NetLogo programming language, the adaptive behavior of an intelligent agent in its environment. The behavior is governed by a behavioral control architecture of biological inspiration that is implemented from state machines. With this type of architecture, the problem addressed is that the agent chooses the most appropriate behavioral response depending on the circumstances of its environment and the stimulation received. The performance of the agent is reported and compared from two experiments using 5 scenarios and 4 controllers. The simulations of this intelligent behavior can be implemented in autonomous mobile robots, assistant agents or tutors, or in those agents that search and retrieve information in databases or the Internet (softbots)

Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments

The transition of mobile robots from a controlled environment towards the real-world represents a major leap in terms of complexity coming primarily from three different factors: partial observability, nondeterminism and dynamic events. To cope with them, robots must achieve some intelligence behaviours to be cost and operationally effective. Two particularly interesting examples of highly complex robotic scenarios are Mars rover missions and the Darpa Robotic Challenge (DRC). In spite of the important differences they present in terms of constraints and requirements, they both have adopted certain level of autonomy to overcome some specific problems. For instance, Mars rovers have been endowed with multiple systems to enable autonomous payload operations and consequently increase science return. In the case of DRC, most teams have autonomous footstep planning or arm trajectory calculation. Even though some specific problems can be addressed with dedicated tools, the general problem remains unsolved: to deploy on-board a reliable reasoning system able to operate robots without human intervention even in complex environments. This is precisely the goal of an automated mission planner. The scientific community has provided plenty of planners able to provide very fast solutions for classical problems, typically characterized by the lack of time and resources representation. Moreover, there are also a handful of applied planners with higher levels of expressiveness at the price of lowest performance. However, a fast, expressive and robust planner has never been used in complex robotic missions. These three properties represent the main drivers for the outcomes of the thesis. To bridge the gap between classical and applied planning, a novel formalism named Hierarchical TimeLine Networks (HTLN) combining Timeline and HTN planning has been proposed. HTLN has been implemented on a mission planner named QuijoteExpress, the first forward-chaining timeline planner to the best of our knowledge. The main idea is to benefit from the great performance of forward-chaining search to resolve temporal problems on the state-space. In addition, QuijoteExpress includes search enhancements such as parallel planning by division of the problem in sub-problems or advanced heuristics management. Regarding expressiveness, the planner incorporates HTN techniques that allow to define hierarchical models and solutions. Finally, plan robustness in uncertain scenarios has been addressed by means of sufficient plans that allow to leave parts of valid plans undefined. To test the planner, a novel lightweight, timeline and ROS-based executive named SanchoExpress has been designed to translate the plans into actions understandable by the different robot subsystems. The entire approach has been tested in two realistic and complementary domains. A cooperative multirover Mars mission and an urban search and rescue mission. The results were extremely positive and opens new promising ways in the field of automated planning applied to robotics

Interface web para monitoramento e controle de segurança com robôs utilizando ROS

This work aims to implement a web system to monitor and control the security of a environment using multiple robots. In the implementation, technologies provided by ROS - Robot Operating System - were used to simulate an environment being patrolled by robots, as well as to do the entire communication process between the web interface and the system. First, a server was implemented to control the security actions and to communicate with the robots. Once the server was ready, a web interface was built to be able to interact with it and show, in a browser, the actions of the robots and the map of the system being patrolled. Finally, it was concluded that, nowadays, with the advancement of communication, browsers and web systems, it is possible to perform all the security control of an environment through the use of robots and a simple computer or cell phone screen. For the simulation, a software called Gazebo was used, with turtlebot robots. The server was developed using the Python programming language. Finally, the JavaScript programming language was used in the web interface, as well as a websocket server and a protocol called Rosbridge, to communicate with the server.Esse trabalho tem como objetivo implementar um sistema web para monitoramento e controle de segurança de um local utilizando múltiplos robôs. Na implementação, foram usadas tecnologias disponibilizadas pelo ROS - Robot Operating System - para simular um ambiente sendo patrulhado por robôs, bem como fazer todo o processo de comunicação entre uma interface web e o sistema. Primeiramente, foi desenvolvido um servidor para controlar as ações de segurança e realizar a comunicação com os robôs. Uma vez que o servidor estava pronto, uma interface web foi construída para poder interagir com o servidor e representar visualmente, em um navegador, as ações dos robôs e o mapa do sistema sendo patrulhado. Verificou-se que, com o avanço da comunicação, dos navegadores e dos sistemas web, é possível realizar todo o controle de segurança de um ambiente por meio do uso de robôs e de uma simples tela de computador ou de celular. Para a simulação, foi utilizado um software chamado Gazebo, com robôs do tipo turtlebot. O servidor foi desenvolvido usando a linguagem de programação Python. Por fim, na interface web foi utilizado a linguagem de programação JavaScript, bem como um servidor websocket e um protocolo chamado Rosbridge, para fazer a comunicação com o servidor

Towards full-scale autonomy for multi-vehicle systems planning and acting in extreme environments

Currently, robotic technology offers flexible platforms for addressing many challenging problems that arise in extreme environments. These problems’ nature enhances the use of heterogeneous multi-vehicle systems which can coordinate and collaborate to achieve a common set of goals. While such applications have previously been explored in limited contexts, long-term deployments in such settings often require an advanced level of autonomy to maintain operability. The success of planning and acting approaches for multi-robot systems are conditioned by including reasoning regarding temporal, resource and knowledge requirements, and world dynamics. Automated planning provides the tools to enable intelligent behaviours in robotic systems. However, whilst many planning approaches and plan execution techniques have been proposed, these solutions highlight an inability to consistently build and execute high-quality plans. Motivated by these challenges, this thesis presents developments advancing state-of-the-art temporal planning and acting to address multi-robot problems. We propose a set of advanced techniques, methods and tools to build a high-level temporal planning and execution system that can devise, execute and monitor plans suitable for long-term missions in extreme environments. We introduce a new task allocation strategy, called HRTA, that optimises the task distribution amongst the heterogeneous fleet, relaxes the planning problem and boosts the plan search. We implement the TraCE planner that enforces contingent planning considering propositional temporal and numeric constraints to deal with partial observability about the initial state. Our developments regarding robust plan execution and mission adaptability include the HLMA, which efficiently optimises the task allocation and refines the planning model considering the experience from robots’ previous mission executions. We introduce the SEA failure solver that, combined with online planning, overcomes unexpected situations during mission execution, deals with joint goals implementation, and enhances mission operability in long-term deployments. Finally, we demonstrate the efficiency of our approaches with a series of experiments using a new set of real-world planning domains.Engineering and Physical Sciences Research Council (EPSRC) grant EP/R026173/

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics