Modular Robots Morphology Transformation And Task Execution

Self-reconfigurable modular robots are composed of a small set of modules with uniform docking interfaces. Different from conventional robots that are custom-built and optimized for specific tasks, modular robots are able to adapt to many different activities and handle hardware and software failures by rearranging their components. This reconfiguration capability allows these systems to exist in a variety of morphologies, and the introduced flexibility enables self-reconfigurable modular robots to handle a much wider range of tasks, but also complicates the design, control, and planning. This thesis considers a hierarchy framework to deploy modular robots in the real world: the robot first identifies its current morphology, then reconfigures itself into a new morphology if needed, and finally executes either manipulation or locomotion tasks. A reliable system architecture is necessary to handle a large number of modules. The number of possible morphologies constructed by modules increases exponentially as the number of modules grows, and these morphologies usually have many degrees of freedom with complex constraints. In this thesis, hardware platforms and several control methods and planning algorithms are developed to build this hierarchy framework leading to the system-level deployment of modular robots, including a hybrid modular robot (SMORES-EP) and a modular truss robot (VTT). Graph representations of modular robots are introduced as well as several algorithms for morphology identification. Efficient mobile-stylereconfiguration strategies are explored for hybrid modular robots, and a real-time planner based on optimal control is developed to perform dexterous manipulation tasks. For modular truss robots, configuration space is studied and a hybrid planning framework (sampling-based and search-based) is presented to handle reconfiguration activities. A non-impact rolling locomotion planner is then developed to drive an arbitrary truss robot in an environment

Control of heterogeneous robot networks for assistance in search and rescue tasks

This project develops a decentralized control strategy for multiple heterogeneous robots oriented to the assistance in search and rescue situations from two complementary perspectives, the discrete tasks allocation and the real-time control. For the discrete task allocation through the mission, we present an optimized algorithm based on events, oriented to the minimization of the time required to attend all the victims in the mission environment. This algorithm allows assign to each robot an appropriate task considering that the robots may vary in their capacity for completing each task and also may vary in their moving capabilities. The considered tasks are the mission environment exploration, the victims’ search and identification, the medical supplies delivery to victims unable to move and the evacuation of victims capable to move. It is worth to mention that, through the development of each task and the estimation of its durations, the robots consider optimized routes considering a distance metric based in the breath first search algorithm called flooding distance with 8 neighbors (8NF) which considers only orthogonal and 45 degrees diagonal movements allowing an estimation of the geodesic distance to each point in the map. Regarding to the real-time control laws, they oversee the proper execution of the tasks assigned by the reallocation algorithm respecting the restrictions in the connectivity range, the obstacles avoidance and the fulfillment of each task. The exploration task is made employing an adaptation of the algorithm DisCoverage presented by [16] which employing a Voronoi cells based tessellation considering the arrival time to each point as reference, allows the determination of the map of non-convex spaces as those that may be found in search and rescue situations. The evasion of obstacles and the preservation of the robots’ links is achieved employing an approach of artificial potentials based in the work of [37]. The interest points related to each task tracking is made employing proportional control loops for each agent identifying the route points within the line of sight and considering optimized routes given by the 8NF flooding distance metric. Additionally, there is presented a heuristic reconfiguration algorithm that allows to change the network topology preserving its connectivity for each instant of time. This complete framework allows a team of autonomous robots to bring valuable assistance in certain search and rescue situations where the human teams may be insufficient, and/or the mission conditions may be harmful for the people considering that even if the robots cannot realize paramedical tasks yet, they can complete multiple useful tasks for reducing the effort and risks of the human teams in that kind of situations. The functioning of those algorithms is presented in non-trivial simulations intended to show the behaviors that emerge in the robots.Resumen: Este proyecto desarrolló una estrategia de control descentralizado para múltiples robots heterogéneos orientada a la asistencia en situaciones de búsqueda y rescate desde dos perspectivas complementarias, la asignación discreta de tareas y el control en tiempo real. Para la asignación discreta de las tareas a los robots a lo largo de la misión, presentamos un algoritmo optimizado de reasignación de tareas basado en eventos, orientado a la minimización del tiempo requerido para atender a todas las víctimas en el ambiente de misión. Este algoritmo permite asignar a cada robot una tarea apropiada considerando que los robots pueden diferir en su capacidad para completar cada tarea, así como también en sus capacidades de movimiento. Las tareas consideradas son la exploración del ambiente de misión, la búsqueda e identificación de víctimas, la entrega de suministros médicos a las víctimas incapaces de moverse y la evacuación de las víctimas capaces de moverse. Cabe destacar que, durante el desarrollo de cada tarea y la estimación de los tiempos de las mismas, los robots consideran rutas optimizadas considerando una métrica de distancia basada en el algoritmo de búsqueda en anchura (Breath first Search) llamada distancia por inundaci´on con 8 vecinos (8NF) la cual considera movimientos netamente ortogonales y diagonales a 45 grados permitiendo una estimación de la distancia geodésica a cada punto en el mapa. Con respecto a las leyes de control en tiempo real, estas están a cargo de la correcta ejecución de las tareas asignadas por el algoritmo de reasignación de tareas respetando las restricciones en el rango de conectividad, la evasión de colisiones y la completa ejecución de cada tarea. La exploración es desarrollada empleando una adaptación del algoritmo DisCoverage presentado por [16] el cuál empleando una teselación basada en celdas de Voronoi con el tiempo de llegada a cada punto como referencia, permite la determinaci´on del mapa de espacios no convexos como los que se pueden encontrar en algunas situaciones de búsqueda y rescate. La evasión de obstáculos y la preservación de los enlaces se realiza a través de un enfoque de potenciales artificiales basándose en el trabajo de [37]. El seguimiento de los puntos de interés relacionados a cada tarea se realiza empleando lazos de control proporcional para cada agente identificando los puntos de ruta dentro de la línea de visión y considerando rutas optimizadas tomando la estimaciói brindada por la métrica de distancia por inundación 8NF. Adicionalmente se presentó un algoritmo de reconfiguración de la red heurístico que permite cambiar la topología de la red manteniendo la conectividad de la misma para cada instante de tiempo. Este marco de trabajo completo permite a un equipo de robots autónomos brindar asistencia valiosa en ciertas situaciones de búsqueda y rescate d´onde los equipos humanos sean insuficientes y/o las condiciones de la misión pueden ser peligrosas para las personas teniendo en cuenta que si bien los robots actualmente no son capaces de realizar tareas paramédicas si son capaces de realizar múltiples tareas útiles para aligerar el trabajo y el riesgo para equipos humanos en estas situaciones. El funcionamiento de estos algoritmos es presentado en simulaciones no triviales en Matlab R buscando presentar los comportamientos que emergen en los robots y adicionalmente fue implementado en una versión simplificada con robots móviles tipo turtlebot y configuraciones simples de robots BioloidMaestrí

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

System Design, Motion Modelling and Planning for a Recon figurable Wheeled Mobile Robot

Over the past ve decades the use of mobile robotic rovers to perform in-situ scienti c investigations on the surfaces of the Moon and Mars has been tremendously in uential in shaping our understanding of these extraterrestrial environments. As robotic missions have evolved there has been a greater desire to explore more unstructured terrain. This has exposed mobility limitations with conventional rover designs such as getting stuck in soft soil or simply not being able to access rugged terrain. Increased mobility and terrain traversability are key requirements when considering designs for next generation planetary rovers. Coupled with these requirements is the need to autonomously navigate unstructured terrain by taking full advantage of increased mobility. To address these issues, a high degree-of-freedom recon gurable platform that is capable of energy intensive legged locomotion in obstacle-rich terrain as well as wheeled locomotion in benign terrain is proposed. The complexities of the planning task that considers the high degree-of-freedom state space of this platform are considerable. A variant of asymptotically optimal sampling-based planners that exploits the presence of dominant sub-spaces within a recon gurable mobile robot's kinematic structure is proposed to increase path quality and ensure platform safety. The contributions of this thesis include: the design and implementation of a highly mobile planetary analogue rover; motion modelling of the platform to enable novel locomotion modes, along with experimental validation of each of these capabilities; the sampling-based HBFMT* planner that hierarchically considers sub-spaces to better guide search of the complete state space; and experimental validation of the planner with the physical platform that demonstrates how the planner exploits the robot's capabilities to uidly transition between various physical geometric con gurations and wheeled/legged locomotion modes