3D Motion Planning using Kinodynamically Feasible Motion Primitives in Unknown Environments

Autonomous vehicles are a great asset to society by helping perform many dangerous or tedious tasks. They have already been successfully employed for many practical applications, such as search and rescue, automated surveillance, exploration and mapping, sample collection, and remote inspection. In order to perform most tasks autonomously, the vehicle must be able to safely and efficiently navigate through its environment. The algorithms and techniques that allow an autonomous vehicle to find traversable paths to its destination defines the set of problems in robotics known as motion planning. This thesis presents a new motion planner that is capable of finding collision-free paths through an unknown environment while satisfying the kinodynamic constraints of the vehicle. This is done using a two step process. In the first step, a collision-free path is generated using a modified Probabilistic Roadmap (PRM) based planner by assuming unexplored areas are obstacle-free. As obstacles are detected, the planner will replan the path as necessary to ensure that it remains collision-free. In complex environments, it is often necessary to increase the size of the PRM graph during the replanning step so that the graph remains connected. However, this causes the algorithm to slow down significantly over time. To mitigate these issues, the novel local sampling and PRM regeneration techniques are used to increase the computational efficiency of the replanning step. The local sampling technique biases the search towards the neighborhood of the obstacle blocking the path. This encourages the planner to generate small detours around the obstacle instead of rerouting the whole path. The PRM regeneration technique is used to remove all non-critical nodes from the PRM graph. This is used to bound the size of the PRM graph so that it does not grow increasingly large over time. In the second step, the collision-free path is transformed into a series of kinodynamically feasible motion primitives using two novel algorithms: the heuristic re-sampling algorithm and the transformation algorithm. The heuristic re-sampling algorithm is a greedy heuristic algorithm that increases the clearance around the path while removing redundant segments. This algorithm can be applied to any piece-wise linear path, and is guaranteed to produce a solution that is at least as good as the initial path. The transformation algorithm is a method to convert a path into a series of kinodynamically feasible motion primitives. It is extremely efficient computationally, and can be applied to any piece-wise linear path. To achieve good computational performance with PRM based planners, it is necessary to use sampling strategies that can efficiently form connected graphs through narrow and complex regions of the configuration space. Many proposed sampling methods attempt to bias the sample density in favor of these difficult to connect areas. However, these methods do not distinguish between samples that lie inside narrow passages and those that lie along convex borders. The orthogonal bridge test is a novel sampling technique that can identify and reject samples that lie along convex borders. This allows connected PRM graphs to be constructed with fewer nodes, which leads to less collision checking and reduced runtimes. The presented algorithms are experimentally verified using an AR.Drone quadrotor unmanned aerial vehicle (UAV) and a custom built skid-steer unmanned ground vehicle (UGV). Using a simple kinematic model and a basic position controller, the AR.Drone is able to traverse a series of motion primitives with less than 0.3 m of crosstrack error. The skid-steer UGV is able to navigate through unknown environments filled with obstacles to reach a desired destination. Furthermore, the observed runtimes of the proposed motion planner suggest that it is fully capable of computing solution paths online. This is an important result, because online computation is necessary for efficient autonomous operations and it can not be achieved with many existing kinodynamic motion planners

Fast Marching Methods in path and motion planning: improvements and high-level applications

Mención Internacional en el título de doctorPath planning is defined as the process to establish the sequence of states a system must go through in order to reach a desired state. Additionally, motion planning (or trajectory planning) aims to compute the sequence of motions (or actions) to take the system from one state to another. In robotics path planning can refer for instance to the waypoints a robot should follow through a maze or the sequence of points a robotic arm has to follow in order to grasp an object. Motion planning is considered a more general problem, since it includes kinodynamic constraints. As motion planning is a more complex problem, it is often solved in a two-level approach: path planning in the first level and then a control layer tries to drive the system along the specified path. However, it is hard to guarantee that the final trajectory will keep the initial characteristics. The objective of this work is to solve different path and motion planning problems under a common framework in order to facilitate the integration of the different algorithms that can be required during the nominal operation of a mobile robot. Also, other related areas such as motion learning are explored using this framework. In order to achieve this, a simple but powerful algorithm called Fast Marching will be used. Originally, it was proposed to solve optimal control problems. However, it has became very useful to other related problems such as path and motion planning. Since Fast Marching was initially proposed, many different alternative approaches have been proposed. Therefore, the first step is to formulate all these methods within a common framework and carry out an exhaustive comparison in order to give a final answer to: which algorithm is the best under which situations? This Thesis shows that the different versions of Fast Marching Methods become useful when applied to motion and path planning problems. Usually, high-level problems as motion learning or robot formation planning are solved with completely different algorithms, as the problem formulation are mixed. Under a common framework, task integration becomes much easier bringing robots closer to everyday applications. The Fast Marching Method has also inspired modern probabilistic methodologies, where computational cost is enormously improved at the cost of bounded, stochastic variations on the resulting paths and trajectories. This Thesis also explores these novel algorithms and their performance.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Antonio Giménez Fernández.- Vocal: Isabel Lobato de Faria Ribeir

An Interpolated Dynamic Navigation Function

The E-star algorithm is a path planning method capable of dynamic replanning and user-configurable path cost interpolation. It calculates a navigation function as a sampling of an underlying smooth goal distance that takes into account a continuous notion of risk that can be controlled in a fine-grained manner. E-star results in more appropriate paths during gradient descent. Dynamic replanning means that changes in the environment model can be repaired to avoid the expenses of complete replanning. This helps compensating for the increased computational effort required for interpolation. We present the theoretical basis and a working implementation, as well as measurements of the algorithm\'s precision, topological correctness, and computational effort

Planning and estimation algorithms for human-like grasping

Mención Internacional en el título de doctorThe use of robots in human-like environments requires them to be able to sense and model unstructured scenarios. Thus, their success will depend on their versatility for interacting with the surroundings. This interaction often includes manipulation of objects for accomplishing common daily tasks. Therefore, robots need to sense, understand, plan and perform; and this has to be a continuous loop. This thesis presents a framework which covers most of the phases encountered in a common manipulation pipeline. First, it is shown how to use the Fast Marching Squared algorithm and a leader-followers strategy to control a formation of robots, simplifying a high dimensional path-planning problem. This approach is evaluated with simulations in complex environments in which the formation control technique is applied. Results are evaluated in terms of distance to obstacles (safety) and the needed deformation. Then, a framework to perform the grasping action is presented. The necessary techniques for environment modelling and grasp synthesis and path planning and control are presented. For the motion planning part, the formation concept from the previous chapter is recycled. This technique is applied to the planning and control of the movement of a complex hand-arm system. Tests using robot Manfred show the possibilities of the framework when performing in real scenarios. Finally, under the assumption that the grasping actions may not always result as it was previously planned, a Bayesian-based state-estimation process is introduced to estimate the final in-hand object pose after a grasping action is done, based on the measurements of proprioceptive and tactile sensors. This approach is evaluated in real experiments with Reex Takktile hand. Results show good performance in general terms, while suggest the need of a vision system for a more precise outcome.La investigación en robótica avanza con la intención de evolucionar hacia el uso de los robots en entornos humanos. A día de hoy, su uso está prácticamente limitado a las fábricas, donde trabajan en entornos controlados realizando tareas repetitivas. Sin embargo, estos robots son incapaces de reaccionar antes los más mínimos cambios en el entorno o en la tarea a realizar. En el grupo de investigación del Roboticslab se ha construido un manipulador móvil, llamado Manfred, en el transcurso de los últimos 15 años. Su objetivo es conseguir realizar tareas de navegación y manipulación en entornos diseñados para seres humanos. Para las tareas de manipulación y agarre, se ha adquirido recientemente una mano robótica diseñada en la universidad de Gifu, Japón. Sin embargo, al comienzo de esta tesis, no se había realzado ningún trabajo destinado a la manipulación o el agarre de objetos. Por lo tanto, existe una motivación clara para investigar en este campo y ampliar las capacidades del robot, aspectos tratados en esta tesis. La primera parte de la tesis muestra la aplicación de un sistema de control de formaciones de robots en 3 dimensiones. El sistema explicado utiliza un esquema de tipo líder-seguidores, y se basa en la utilización del algoritmo Fast Marching Square para el cálculo de la trayectoria del líder. Después, mientras el líder recorre el camino, la formación se va adaptando al entorno para evitar la colisión de los robots con los obstáculos. El esquema de deformación presentado se basa en la información sobre el entorno previamente calculada con Fast Marching Square. El algoritmo es probado a través de distintas simulaciones en escenarios complejos. Los resultados son analizados estudiando principalmente dos características: cantidad de deformación necesaria y seguridad de los caminos de los robots. Aunque los resultados son satisfactorios en ambos aspectos, es deseable que en un futuro se realicen simulaciones más realistas y, finalmente, se implemente el sistema en robots reales. El siguiente capítulo nace de la misma idea, el control de formaciones de robots. Este concepto es usado para modelar el sistema brazo-mano del robot Manfred. Al igual que en el caso de una formación de robots, el sistema al completo incluye un número muy elevado de grados de libertad que dificulta la planificación de trayectorias. Sin embargo, la adaptación del esquema de control de formaciones para el brazo-mano robótico nos permite reducir la complejidad a la hora de hacer la planificación de trayectorias. Al igual que antes, el sistema se basa en el uso de Fast Marching Square. Además, se ha construido un esquema completo que permite modelar el entorno, calcular posibles posiciones para el agarre, y planificar los movimientos para realizarlo. Todo ello ha sido implementado en el robot Manfred, realizando pruebas de agarre con objetos reales. Los resultados muestran el potencial del uso de este esquema de control, dejando lugar para mejoras, fundamentalmente en el apartado de la modelización de objetos y en el cálculo y elección de los posibles agarres. A continuación, se trata de cerrar el lazo de control en el agarre de objetos. Una vez un sistema robótico ha realizado los movimientos necesarios para obtener un agarre estable, la posición final del objeto dentro de la mano resulta, en la mayoría de las ocasiones, distinta de la que se había planificado. Este hecho es debido a la acumulación de fallos en los sistemas de percepción y modelado del entorno, y los de planificación y ejecución de movimientos. Por ello, se propone un sistema Bayesiano basado en un filtro de partículas que, teniendo en cuenta la posición de la palma y los dedos de la mano, los datos de sensores táctiles y la forma del objeto, estima la posición del objeto dentro de la mano. El sistema parte de una posición inicial conocida, y empieza a ejecutarse después del primer contacto entre los dedos y el objeto, de manera que sea capaz de detectar los movimientos que se producen al realizar la fuerza necesaria para estabilizar el agarre. Los resultados muestran la validez del método. Sin embargo, también queda claro que, usando únicamente la información táctil y de posición, hay grados de libertad que no se pueden determinar, por lo que, para el futuro, resultaría aconsejable la combinación de este sistema con otro basado en visión. Finalmente se incluyen 2 anexos que profundizan en la implementación de la solución del algoritmo de Fast Marching y la presentación de los sistemas robóticos reales que se han usado en las distintas pruebas de la tesis.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Raúl Suárez Feijoo.- Vocal: Pedro U. Lim

Dexterous robotic motion planning using intelligent algorithms

The fundamental purpose of robots is to help humans in a variety of difficult tasks, enabling people to increase their capabilities of strength, energy, speed, memory, and to operate in hazardous environments and many other applications. Service robots, more precisely mobile manipulators, incorporate one or two robotic arms and a mobile base, and must accomplish complex manipulations tasks, interacting with tools or objects and navigating through cluttered environments. To this end, the motion planning problem plays a key role in the ahead calculation of robot movements to interact with its world and achieve the established goals. The objective of this work is to design various motion planning methods in order to improve the autonomy of MANFRED-2, which is a mobile robot fully developed by the Robotics Lab research group of the Systems Engineering and Automation Department of the Carlos III University of Madrid. Mobile robots need to calculate accurate paths in order to navigate and interact with objects throughout their surrounding area. In this work, we have developed motion planning algorithms for both navigation and manipulation. The presented algorithms for path planning are based on the Fast Marching Square method and include a replanner with subgoals, an anytime triangular planner, and a nonholonomic approach. The replanner with subgoals starts by generating a smooth and safe global path with the Fast Marching Square method. Then, this path is divided into multiple subpaths separated by equidistant nodes (defined by topological or metric constraints). After that, the obstacles information is progressively added and modifications are made only when the original path is unreachable. The most important advantage with respect to similar approaches is that the generated sub-paths are always efficient in terms of smoothness and safeness. Besides, the computational cost is low enough to use the algorithm in real-time. The anytime triangular planner, such as “Anytime” algorithms, quickly finds a feasible but not necessarily optimal motion plan which is incrementally improved. One important characteristic that this type of algorithms must satisfy is that the path must be generated in real-time. The planner relies on the Fast Marching Square method over a triangular mesh structure. Different variants are introduced and compared under equal circumstances that produce different paths in response time and quality, which leads us to an additional consideration. As in the field of benchmarking it is becoming increasingly difficult to compare new planners approaches because of the lack of a general benchmarking platform, improvements to existing approaches are suggested. Finally, the nonholonomic approach is presented. It is based on the Fast Marching Square method and its application to car-like robots. In order to apply the proposed method, a three dimensional configuration space of the environment is considered. The first two dimensions are given by the position of the robot, and the third one by its orientation. This means that we operate over the configuration space instead of the bi-dimensional environment map. Besides, the trajectory is computed along the configuration space taking into account the dimensions of the vehicle. In this way, it is possible to guarantee the absence of collisions. The proposed method is consistent at local and global scale because it guarantees a motion path solution, if it exists, and does not require global replanning supervision when a local trap is detected. Once a mobile robot has reached a goal location, it usually triggers the servomotors enclosed inside its robotic arm to manipulate a target. The manipulation algorithms presented in this work include the adaptation of trajectories, a planner with adaptive dimensionality, and an implementation of a dimensionality reduction approach inside a nuclear device. The adaptation of manipulation trajectories enables the robot to accomplish a task in different locations by using Evolution Strategies and forward kinematics. This approach avoids all the inconveniences that inverse kinematics imply, as well as the convergence problems in singular kinematic configurations. The planner with adaptive dimensionality reduces the complexity of high-dimensional path planning. First, a Rapidly-exploring Random Tree trajectory is generated using the full degrees of freedom of the robotic arm. Then, a geometry as a closed tube is built around the path line and the Fast Marching Square method is applied from start to goal using three dimensions inside the surface. The resulted three dimensional path is converted to full degrees of freedom with the inverse kinematics of the robot. The result is a smoother and safer path, visually more human friendly. Additionally, the search space is reduced, and therefore, also the planning time and the memory requirements. The application inside the nuclear device, similarly to the previous approach, reduces the degrees of freedom of the problem (but this time to two dimensions due to the mostly planar nature of the robot). The manipulation path is smooth and safe in an environment where safety must be the primarily objective. The motion planning algorithms have been tested in numerous experiments. The fast response of the methods allows its application in real-time tasks.El propósito fundamental de los robots es ayudar a los humanos en tareas difíciles, lo que permite a las personas incrementar sus capacidades de fuerza, energía, velocidad y memoria para trabajar en entornos peligrosos y en una inmensa variedad de aplicaciones. Los robots de servicio, puntualmente los manipuladores móviles, incorporan uno o dos brazos robóticos y una base móvil, y deben ser capaces de realizar tareas complejas de manipulación, interactuando con herramientas u objetos y navegando a través de entornos con obstáculos. Para este fin, el problema de la planificación de movimientos juega un rol clave en el cálculo anticipado de los movimientos del robot, para interactuar con su mundo y realizar las tareas establecidas. El objetivo de este trabajo es diseñar diversos métodos de planificación de movimiento con el fin de mejorar la autonomía de MANFRED-2, un robot móvil que fue desarrollado completamente en el grupo de investigación del Laboratorio de Robótica del Departamento de Ingeniería de Sistemas y Automatización de la Universidad Carlos III de Madrid. Los robots móviles necesitan calcular de antemano trayectorias precisas para poder navegar e interactuar con objetos en su entorno. En este trabajo, hemos desarrollado algoritmos de planificación de movimiento para navegación y manipulación robótica. Los algoritmos presentados para la planificación de trayectorias de navegación se basan en el método de Fast Marching Square (FM2) e incluyen un replanificador con sub-objetivos, un planificador triangular de tipo interrumpible (en inglés este enfoque es mejor conocido como Anytime), y un enfoque no holonómico. El replanificador con submetas comienza generando una trayectoria global de curvas suaves y segura con FM2, entonces este camino es dividido en múltiples subtrayectorias separadas por nodos equidistantes (definidos por restricciones topológicas o métricas). Después de esto, se actualiza progresivamente el entorno con obstáculos detectados por los sensores; sólo se realizan cambios cuando la trayectoria original resulta inalcanzable. La ventaja más importante con respecto a enfoques similares es que las sub-trayectorias generadas son siempre eficientes en términos de suavidad y seguridad. Además, el coste computacional es lo suficientemente bajo como para utilizar el algoritmo en tiempo real. El planificador triangular interrumpible, como algoritmo “Anytime”, encuentra rápidamente una trayectoria de navegación válida, pero no necesariamente ópti ma, a continuación, de forma incremental se va mejorando según haya tiempo hasta llegar al óptimo. La capacidad más resaltante de este tipo de algoritmos es la de generar trayectorias en tiempo real. El planificador se basa en el uso de FM2 sobre una estructura de malla triangular. Se presentan diferentes formas de construir el mallado y se comparan en igualdad de circunstancias los diferentes caminos producidos en tiempo de respuesta y calidad, lo que generó una contribución adicional. Debido a la falta de una plataforma general de evaluación robusta, en el campo de la evaluación de trayectorias es cada vez más difícil comparar nuevos planificadores, por consiguiente se sugieren mejoras a los enfoques existentes. Finalmente, se presenta el enfoque no holónomo, que se basa en FM2 y su aplicación en robots móviles con sistemas de dirección similares a la de los coches. Para aplicar el método propuesto, se considera un espacio de configuración tridimensional del entorno, donde las dos primeras dimensiones vienen dadas por la posición del robot y la tercera dimensión, por su orientación. Esto quiere decir, que operamos en el espacio de configuraciones en vez de en el mapa bidimensional del entorno. Además, la trayectoria se calcula en el espacio de configuraciones teniendo en cuenta las dimensiones del vehículo, de esta manera es posible garantizar la ausencia de colisiones. El método propuesto es consistente a nivel local y global, ya que si existe una solución se garantiza encontrarla, y no requiere de supervisión global para reiniciar una planificación cuando se detecta un bloqueo a nivel local. Una vez que el robot móvil ha alcanzado la ubicación requerida, se suelen accionar los servomotores que están dentro del brazo robótico para manipular un objeto. Los algoritmos de manipulación presentados en este trabajo incluyen la adaptación de trayectorias, un planificador con dimensionalidad adaptable, y una implementación de un método de reducción de la dimensionalidad dentro de un dispositivo nuclear. La adaptación de las trayectorias de manipulación permite al robot realizar una misma tarea con diferentes ubicaciones y orientaciones haciendo uso de una Evolution Strategy y la cinemática directa del robot, este enfoque evita todos los inconvenientes que implican utilizar la cinemática inversa, así como los problemas de convergencia en configuraciones cinemáticas singulares. El planificador con dimensionalidad adaptativa reduce la complejidad de la planificación de trayectorias de manipulación con muchas dimensiones; en primer lugar, una trayectoria RRT se genera utilizando todos los grados de libertad (DOF) del brazo robótico, a continuación, una figura geométrica en forma de tubo cerrado se construye alrededor de la línea de la trayectoria y se aplica FM2 dentro de la superficie desde el inicio hasta el objetivo utilizando tres dimensiones, la ruta 3D resultante se convierte con la cinemática inversa del robot. El resultado es un camino más suave y seguro, más amigable a la vista humana. Además, debido a que el espacio de búsqueda se reduce, también se reduce el tiempo de planificación y los requerimientos de memoria. La aplicación en el interior del dispositivo nuclear, de manera similar al enfoque anterior, reduce los DOF del problema pero esta vez a dos dimensiones aprovechando la naturaleza mayormente plana del robot utilizado. La trayectoria de manipulación es suave y segura, lo que es conveniente en un entorno donde la seguridad debe ser el objetivo principal. Los algoritmos de planificación de movimiento resultantes han sido probados en numerosos experimentos. La respuesta rápida de los métodos permite su aplicación en tiempo real.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Carlos Sagüés Blázquiz.- Vocal: Pedro Lim

Motion planning and obstacle avoidance for mobile robots in highly cluttered dynamic environments

After a quarter century of mobile robot research, applications of this fascinating technology appear in real-world settings. Some require operation in environments that are densely cluttered with moving obstacles. Public mass exhibitions or conventions are examples of such challenging environments. This dissertation addresses the navigational challenges that arise in settings where mobile robots move among people and possibly need to directly interact with humans who are not used to dealing with technical details. Two important aspects are solved: Reliable reactive obstacle avoidance to guarantee safe operation, and smooth path planning that allows to dynamically adapt environment information to the motion of surrounding persons and objects. Given the existing body of research results in the field of obstacle avoidance and path planning, which is reviewed in this context, particular attention is paid to integration aspects for leveraging advantages while compensating drawbacks of various methods. In particular, grid-based wavefront propagation (NF1 and fast marching level set methods), dynamic path representation (bubble band concept), and high-fidelity execution (dynamic window approach) are combined in novel ways. Experiments demonstrate the robustness of the obstacle avoidance and path planning systems

Computational Methods for Cognitive and Cooperative Robotics

In the last decades design methods in control engineering made substantial progress in the areas of robotics and computer animation. Nowadays these methods incorporate the newest developments in machine learning and artificial intelligence. But the problems of flexible and online-adaptive combinations of motor behaviors remain challenging for human-like animations and for humanoid robotics. In this context, biologically-motivated methods for the analysis and re-synthesis of human motor programs provide new insights in and models for the anticipatory motion synthesis. This thesis presents the author’s achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter “Goal-directed Imitation for Robots” considers imitation learning in cognitive and developmental robotics. The work presented here details the author’s progress in the development of hierarchical motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of ‘learning for imitation’ and ‘learning by imitation’. The complete system includes a low-level real-time capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts. Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the problems of task-related action transitions are considered in the second part of the thesis “Kinematic Motion Synthesis for Computer Graphics and Robotics”. In this part, a new approach of modeling complex full-body human actions by mixtures of time-shift invariant motor primitives in presented. The online-capable full-body motion generation architecture based on dynamic movement primitives driving the time-shift invariant motor synergies was implemented as an online-reactive adaptive motion synthesis for computer graphics and robotics applications. The last chapter of the thesis entitled “Contraction Theory and Self-organized Scenarios in Computer Graphics and Robotics” is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last part presents new mathematical tools for stability analysis and synthesis of multi-agent cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der künstlichen Intelligenz. Die flexible und echtzeitfähige Kombination von motorischen Verhaltensweisen ist eine wesentliche Herausforderung für die Generierung menschenähnlicher Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme neue Erkenntnisse und Modelle für die antizipatorische Bewegungssynthese. Diese Dissertation präsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der Dissertation im Kapitel “Zielgerichtete Nachahmung für Roboter” behandelt das Imitationslernen in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben neue Methoden für die hierarchische Bewegungserkennung und -planung, die durch Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert wurden. Die entwickelte Architektur ist in der Lage, ‘durch Imitation zu lernen’ und ‘zu lernen zu imitieren’. Das komplette entwickelte System enthält ein echtzeitfähiges Pfadplanungssubsystem zur Hindernisvermeidung während der Durchführung von Armbewegungen. Das lernbasierte Pfadplanungssubsystem ist universell und für alle Arten von anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer Handlungen zu übertragen. Im zweiten Teil der Arbeit “Kinematische Bewegungssynthese für Computergrafik und Robotik” werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h. von räumlichen und räumlich-zeitlichen Kombinationen motorischer Bewegungselemente bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs übergängen behandelt. Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem wurde ein online-fähiger Synthesealgorithmus für Ganzköperbewegungen entwickelt, der auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde für verschiedene Probleme der Bewegungssynthese für die Computergrafik- und Roboteranwendungen implementiert. Das letzte Kapitel der Dissertation mit dem Titel “Kontraktionstheorie und selbstorganisierte Szenarien in der Computergrafik und Robotik” widmet sich optimalen Kontrollstrategien in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare Kinematik gekennzeichnet sind. Dieser letzte Teil präsentiert neue mathematische Werkzeuge für die Stabilitätsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien

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

Motion Planning under Uncertainty for Autonomous Navigation of Mobile Robots and UAVs

This thesis presents a reliable and efficient motion planning approach based on state lattices for the autonomous navigation of mobile robots and UAVs. The proposal retrieves optimal paths in terms of safety and traversal time, and deals with the kinematic constraints and the motion and sensing uncertainty at planning time. The efficiency is improved by a novel graduated fidelity state lattice which adapts to the obstacles in the map and the maneuverability of the robot, and by a new multi-resolution heuristic which reduces the computational complexity. The motion planner also includes a novel method to reliably estimate the probability of collision of the paths considering the uncertainty in heading and the robot dimensions

3D mapping and path planning from range data

This thesis reports research on mapping, terrain classification and path planning. These are classical problems in robotics, typically studied independently, and here we link such problems by framing them within a common proprioceptive modality, that of three-dimensional laser range scanning. The ultimate goal is to deliver navigation paths for challenging mobile robotics scenarios. For this reason we also deliver safe traversable regions from a previously computed globally consistent map. We first examine the problem of registering dense point clouds acquired at different instances in time. We contribute with a novel range registration mechanism for pairs of 3D range scans using point-to-point and point-to-line correspondences in a hierarchical correspondence search strategy. For the minimization we adopt a metric that takes into account not only the distance between corresponding points, but also the orientation of their relative reference frames. We also propose FaMSA, a fast technique for multi-scan point cloud alignment that takes advantage of the asserted point correspondences during sequential scan matching, using the point match history to speed up the computation of new scan matches. To properly propagate the model of the sensor noise and the scan matching, we employ first order error propagation, and to correct the error accumulation from local data alignment, we consider the probabilistic alignment of 3D point clouds using a delayed-state Extended Information Filter (EIF). In this thesis we adapt the Pose SLAM algorithm to the case of 3D range mapping, Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where sensor data is solely used to produce relative constraints between robot poses. These dense mapping techniques are tested in several scenarios acquired with our 3D sensors, producing impressively rich 3D environment models. The computed maps are then processed to identify traversable regions and to plan navigation sequences. In this thesis we present a pair of methods to attain high-level off-line classification of traversable areas, in which training data is acquired automatically from navigation sequences. Traversable features came from the robot footprint samples during manual robot motion, allowing us to capture terrain constrains not easy to model. Using only some of the traversed areas as positive training samples, our algorithms are tested in real scenarios to find the rest of the traversable terrain, and are compared with a naive parametric and some variants of the Support Vector Machine. Later, we contribute with a path planner that guarantees reachability at a desired robot pose with significantly lower computation time than competing alternatives. To search for the best path, our planner incrementally builds a tree using the A* algorithm, it includes a hybrid cost policy to efficiently expand the search tree, combining random sampling from the continuous space of kinematically feasible motion commands with a cost to goal metric that also takes into account the vehicle nonholonomic constraints. The planer also allows for node rewiring, and to speed up node search, our method includes heuristics that penalize node expansion near obstacles, and that limit the number of explored nodes. The method book-keeps visited cells in the configuration space, and disallows node expansion at those configurations in the first full iteration of the algorithm. We validate the proposed methods with experiments in extensive real scenarios from different very complex 3D outdoors environments, and compare it with other techniques such as the A*, RRT and RRT* algorithms.Esta tesis reporta investigación sobre el mapeo, clasificación de terreno y planificación de trayectorias. Estos son problemas clásicos en robótica los cuales generalmente se estudian de forma independiente, aquí se vinculan enmarcandolos con una modalidad propioceptiva común: un láser de rango 3D. El objetivo final es ofrecer trayectorias de navegación para escenarios complejos en el marco de la robótica móvil. Por esta razón también entregamos regiones transitables en un mapa global consistente calculado previamente. Primero examinamos el problema de registro de nubes de puntos adquiridas en diferentes instancias de tiempo. Contribuimos con un novedoso mecanismo de registro de pares de imagenes de rango 3D usando correspondencias punto a punto y punto a línea, en una estrategia de búsqueda de correspondencias jerárquica. Para la minimización optamos por una metrica que considera no sólo la distancia entre puntos, sino también la orientación de los marcos de referencia relativos. También proponemos FAMSA, una técnica para el registro rápido simultaneo de multiples nubes de puntos, la cual aprovecha las correspondencias de puntos obtenidas durante el registro secuencial, usando inicialmente la historia de correspondencias para acelerar el cálculo de las correspondecias en los nuevos registros de imagenes. Para propagar adecuadamente el modelo del ruido del sensor y del registro de imagenes, empleamos la propagación de error de primer orden, y para corregir el error acumulado del registro local, consideramos la alineación probabilística de nubes de puntos 3D utilizando un Filtro Extendido de Información de estados retrasados. En esta tesis adaptamos el algóritmo Pose SLAM para el caso de mapas con imagenes de rango 3D, Pose SLAM es la variante de SLAM donde solamente se estima la trayectoria del robot, usando los datos del sensor como restricciones relativas entre las poses robot. Estas técnicas de mapeo se prueban en varios escenarios adquiridos con nuestros sensores 3D produciendo modelos 3D impresionantes. Los mapas obtenidos se procesan para identificar regiones navegables y para planificar secuencias de navegación. Presentamos un par de métodos para lograr la clasificación de zonas transitables fuera de línea. Los datos de entrenamiento se adquieren de forma automática usando secuencias de navegación obtenidas manualmente. Las características transitables se captan de las huella de la trayectoria del robot, lo cual permite capturar restricciones del terreno difíciles de modelar. Con sólo algunas de las zonas transitables como muestras de entrenamiento positivo, nuestros algoritmos se prueban en escenarios reales para encontrar el resto del terreno transitable. Los algoritmos se comparan con algunas variantes de la máquina de soporte de vectores (SVM) y una parametrizacion ingenua. También, contribuimos con un planificador de trayectorias que garantiza llegar a una posicion deseada del robot en significante menor tiempo de cálculo a otras alternativas. Para buscar el mejor camino, nuestro planificador emplea un arbol de busqueda incremental basado en el algoritmo A*. Incluimos una póliza de coste híbrido para crecer de manera eficiente el árbol, combinando el muestro aleatorio del espacio continuo de comandos cinemáticos del robot con una métrica de coste al objetivo que también concidera las cinemática del robot. El planificador además permite reconectado de nodos, y, para acelerar la búsqueda de nodos, se incluye una heurística que penaliza la expansión de nodos cerca de los obstáculos, que limita el número de nodos explorados. El método conoce las céldas que ha visitado del espacio de configuraciones, evitando la expansión de nodos en configuraciones que han sido vistadas en la primera iteración completa del algoritmo. Los métodos propuestos se validán con amplios experimentos con escenarios reales en diferentes entornos exteriores, asi como su comparación con otras técnicas como los algoritmos A*, RRT y RRT*.Postprint (published version