Path Planning Framework for Unmanned Ground Vehicles on Uneven Terrain

In this thesis, I address the problem of long-range path planning on uneven terrain for non-holonomic wheeled mobile robots (WMR). Uneven terrain path planning is essential for search-and-rescue, surveillance, military, humanitarian, agricultural, constructing missions, etc. These missions necessitate the generation of a feasible sequence of waypoints, or reference states, to navigate a WMR from the initial location to the final target location through the uneven terrain. The feasibility of navigating through a given path over uneven terrain can be undermined by various terrain features. Examples of such features are loose soil, vegetation, boulders, steeply sloped terrain, or a combination of all of these elements. I propose a three-stage framework to solve the problem of rapid long-range path planning. In the first stage, RRT-Connect provides a rapid discovery of the feasible solution. Afterward, Informed RRT* improves the feasible solution. Finally, Shortcut heuristics improves the solution locally. To improve the computational speed of path planning algorithms, we developed an accelerated version of the traversability estimation on point clouds based on Principal Component Analysis. The benchmarks demonstrate the efficacy of the path planning approach

Computing fast search heuristics for physics-based mobile robot motion planning

Mobile robots are increasingly being employed to assist responders in search and rescue missions. Robots have to navigate in dangerous areas such as collapsed buildings and hazardous sites, which can be inaccessible to humans. Tele-operating the robots can be stressing for the human operators, which are also overloaded with mission tasks and coordination overhead, so it is important to provide the robot with some degree of autonomy, to lighten up the task for the human operator and also to ensure robot safety. Moving robots around requires reasoning, including interpretation of the environment, spatial reasoning, planning of actions (motion), and execution. This is particularly challenging when the environment is unstructured, and the terrain is \textit{harsh}, i.e. not flat and cluttered with obstacles. Approaches reducing the problem to a 2D path planning problem fall short, and many of those who reason about the problem in 3D don't do it in a complete and exhaustive manner. The approach proposed in this thesis is to use rigid body simulation to obtain a more truthful model of the reality, i.e. of the interaction between the robot and the environment. Such a simulation obeys the laws of physics, takes into account the geometry of the environment, the geometry of the robot, and any dynamic constraints that may be in place. The physics-based motion planning approach by itself is also highly intractable due to the computational load required to perform state propagation combined with the exponential blowup of planning; additionally, there are more technical limitations that disallow us to use things such as state sampling or state steering, which are known to be effective in solving the problem in simpler domains. The proposed solution to this problem is to compute heuristics that can bias the search towards the goal, so as to quickly converge towards the solution. With such a model, the search space is a rich space, which can only contain states which are physically reachable by the robot, and also tells us enough information about the safety of the robot itself. The overall result is that by using this framework the robot engineer has a simpler job of encoding the \textit{domain knowledge} which now consists only of providing the robot geometric model plus any constraints

Hybrid terrain traversability analysis in off-road environments

There is a significant growth in autonomy level in off-road ground vehicles. However, unknown off-road environments are often challenging due to their unstructured and rough nature. To find a path that the robot can move smoothly to its destination, it needs to analyse the surrounding terrain. In this paper, we present a hybrid terrain traversability analysis framework. Semantic segmentation is implemented to understand different types of the terrain surrounding the robot; meanwhile geometrical properties of the terrain are assessed with the aid of a probabilistic terrain estimation. The framework represents the traversability analysis on a robot-centric cost map, which is available to the path planners. We evaluated the proposed framework with synchronised sensor data captured while driving the robot in real off-road environments. This thorough terrain traversability analysis will be crucial for autonomous navigation systems in off-road environments

Visual Odometry and Traversability Analysis for Wheeled Robots in Complex Environments

Durch die technische Entwicklung im Bereich der radbasierten mobilen Roboter (WMRs) erweitern sich deren Anwendungsszenarien. Neben den eher strukturierten industriellen und häuslichen Umgebungen sind nun komplexere städtische Szenarien oder Außenbereiche mögliche Einsatzgebiete. Einer dieser neuen Anwendungsfälle wird in dieser Arbeit beschrieben: ein intelligenter persönlicher Mobilitätsassistent, basierend auf einem elektrischen Rollator. Ein solches System hat mehrere Anforderungen: Es muss sicher, robust, leicht und preiswert sein und sollte in der Lage sein, in Echtzeit zu navigieren, um eine direkte physische Interaktion mit dem Benutzer zu ermöglichen. Da diese Eigenschaften für fast alle Arten von WMRs wünschenswert sind, können alle in dieser Arbeit präsentierten Methoden auch mit anderen Typen von WMRs verwendet werden. Zuerst wird eine visuelle Odometriemethode vorgestellt, welche auf die Arbeit mit einer nach unten gerichteten RGB-D-Kamera ausgelegt ist. Hierzu wird die Umgebung auf die Bodenebene projiziert, um eine 2-dimensionale Repräsentation zu erhalten. Nun wird ein effizientes Bildausrichtungsverfahren verwendet, um die Fahrzeugbewegung aus aufeinander folgenden Bildern zu schätzen. Da das Verfahren für den Einsatz auf einem WMR ausgelegt ist, können weitere Annahmen verwendet werden, um die Genauigkeit der visuellen Odometrie zu verbessern. Für einen nicht-holonomischen WMR mit einem bekannten Fahrzeugmodell, entweder Differentialantrieb, Skid-Lenkung oder Ackermann-Lenkung, können die Bewegungsparameter direkt aus den Bilddaten geschätzt werden. Dies verbessert die Genauigkeit und Robustheit des Verfahrens erheblich. Zusätzlich wird eine Ausreißererkennung vorgestellt, die im Modellraum, d.h. den Bewegungsparametern des kinematischen Models, arbeitet. Üblicherweise wird die Ausreißererkennung im Datenraum, d.h. auf den Bildpunkten, durchgeführt. Mittels der Projektion der Umgebung auf die Bodenebene kann auch eine Höhenkarte der Umgebung erstellt werde. Es wird untersucht, ob diese Karte, in Verbindung mit einem detaillierten Fahrzeugmodell, zur Abschätzung zukünftiger Fahrzeugposen verwendet werden kann. Durch die Verwendung einer gemeinsamen bildbasierten Darstellung der Umgebung und des Fahrzeugs wird eine sehr effiziente und dennoch sehr genaue Posenschätzmethode vorgeschlagen. Da die Befahrbarkeit eines Bereichs durch die Fahrzeugposen und mögliche Kollisionen bestimmt werden kann, wird diese Methode für eine neue echtzeitfähige Pfadplanung verwendet. Aus der Fahrzeugpose werden verschiedene Sicherheitskriterien bestimmt, die als Heuristik für einen A*-ähnlichen Planer verwendet werden. Hierzu werden mithilfe des kinematischen Models mögliche zukünftige Fahrzeugposen ermittelt und für jede dieser Posen ein Befahrbarkeitswert berechnet.Das endgültige System ermöglicht eine sichere und robuste Echtzeit-Navigation auch in schwierigen Innen- und Außenumgebungen.The application of wheeled mobile robots (WMRs) is currently expanding from rather controlled industrial or domestic scenarios into more complex urban or outdoor environments, allowing a variety of new use cases. One of these new use cases is described in this thesis: An intelligent personal mobility assistant, based on an electrical rollator. Such a system comes with several requirements: It must be safe and robust, lightweight, inexpensive and should be able to navigate in real-time in order to allow direct physical interaction with the user. As these properties are desirable for most WMRs, all methods proposed in this thesis can also be used with other WMR platforms.First, a visual odometry method is presented, which is tailored to work with a downward facing RGB-D camera. It projects the environment onto a ground plane image and uses an efficient image alignment method to estimate the vehicle motion from consecutive images. As the method is designed for use on a WMR, further constraints can be employed to improve the accuracy of the visual odometry. For a non-holonomic WMR with a known vehicle model, either differential drive, skid steering or Ackermann, the motion parameters of the corresponding kinematic model, instead of the generic motion parameters, can be estimated directly from the image data. This significantly improves the accuracyand robustness of the method. Additionally, an outlier rejection scheme is presented that operates in model space, i.e. the motion parameters of the kinematic model, instead of data space, i.e. image pixels. Furthermore, the projection of the environment onto the ground plane can also be used to create an elevation map of the environment. It is investigated if this map, in conjunction with a detailed vehicle model, can be used to estimate future vehicle poses. By using a common image-based representation of the environment and the vehicle, a very efficient and still highly accurate pose estimation method is proposed. Since the traversability of an area can be determined by the vehicle poses and potential collisions, the pose estimation method is employed to create a novel real-time path planning method. The detailed vehicle model is extended to also represent the vehicle’s chassis for collision detection. Guided by an A*-like planner, a search graph is constructed by propagating the vehicle using its kinematic model to possible future poses and calculating a traversability score for each of these poses. The final system performs safe and robust real-time navigation even in challenging indoor and outdoor environments

Vision-based legged robot navigation: localisation, local planning, learning

The recent advances in legged locomotion control have made legged robots walk up staircases, go deep into underground caves, and walk in the forest. Nevertheless, autonomously achieving this task is still a challenge. Navigating and acomplishing missions in the wild relies not only on robust low-level controllers but also higher-level representations and perceptual systems that are aware of the robot's capabilities. This thesis addresses the navigation problem for legged robots. The contributions are four systems designed to exploit unique characteristics of these platforms, from the sensing setup to their advanced mobility skills over different terrain. The systems address localisation, scene understanding, and local planning, and advance the capabilities of legged robots in challenging environments. The first contribution tackles localisation with multi-camera setups available on legged platforms. It proposes a strategy to actively switch between the cameras and stay localised while operating in a visual teach and repeat context---in spite of transient changes in the environment. The second contribution focuses on local planning, effectively adding a safety layer for robot navigation. The approach uses a local map built on-the-fly to generate efficient vector field representations that enable fast and reactive navigation. The third contribution demonstrates how to improve local planning in natural environments by learning robot-specific traversability from demonstrations. The approach leverages classical and learning-based methods to enable online, onboard traversability learning. These systems are demonstrated via different robot deployments on industrial facilities, underground mines, and parklands. The thesis concludes by presenting a real-world application: an autonomous forest inventory system with legged robots. This last contribution presents a mission planning system for autonomous surveying as well as a data analysis pipeline to extract forestry attributes. The approach was experimentally validated in a field campaign in Finland, evidencing the potential that legged platforms offer for future applications in the wild

A Ground Robot for Search And Rescue in Hostile Environment

The recent sheer developments in the field of robotics has encouraged the researcher to consider the robots assisting human in different aspects of life. In this context, search and rescue is a very interesting ambient where the capabilities offered by the robots can be used to not only augment the quality of service but also impose lower risk to the human members of the rescue team. To this purpose, project SHERPA has been defined to investigate an intelligent heterogeneous robotic team in a search and rescue mission. The robotic team includes flying robots such as fixed wing and quad copters for the purpose of patrolling and surveillance and a ground rover that is mainly considered to provide a mobile power replenishment service for the quadrotors. Navigation of the ground rover on the unstructured outdoor environment defined by the SHERPA is of the main focuses of this thesis. Due to roughness of the terrain, there are a lot of issues on the way of a successful localization. Moreover, the planning has to be compatible with the robot and environment constraints to avoid imposing a risk of mechanical damage to the system. To accomplish the battery exchange operation, the rover is equipped with two auxiliary devices namely "Sherpa box" and "Sherpa robotic arm". In this thesis, firstly, designs of the two devices are introduced to the reader in details. Secondly, their integration with the ground rover will be covered. Finally two important benchmarks of the SHERPA project, namely "human leashing" and "battery exchange operation", will be addressed

Navegación reactiva del robot móvil Andábata en entornos naturales

En esta tesis se abordan posibles soluciones para la navegación reactiva del robot móvil terrestre Andábata, cuyo principal sensor exteroceptivo es un escáner láser tridimensional (3D), a baja velocidad en entornos naturales, donde se presentan numerosos retos debido a la complejidad del terreno. Con este fin, se emplea el Robot Operating System (ROS) para organizar el software del robot y procesar la información procedente de los sensores a bordo y llevar a cabo diversas estrategias de navegación. La primera estrategia consiste en construir mapas de elevación borrosos que describan el entorno cercano al vehículo. Dicha estrategia es bastante novedosa pero conlleva una alta carga computacional que reduce la reactividad del robot. Para aliviar esta carga, se propone el entrenamiento clasi ficadores de transitabilidad del terreno mediante aprendizaje supervisado. Para ello es necesario disponer de nubes de puntos 3D etiquetadas sin error que se han generado de forma sintética en el simulador robótico Gazebo. Finalmente, se analiza una nueva estrategia de navegación local basada en la clasificación de los puntos obtenidos por el telémetro láser 3D. Para ello, se construyen mapas de transitabilidad bidimensionales (2D) que permiten que Andábata evite de forma reactiva obstáculos tanto negativos como positivos

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

Hybrid PSO-PWL-Dijkstra approach for path planning of non holonomic platforms in dense contexts

Planning is an essential capability for autonomous robots. Many applications impose a diversity of constraints and traversing costs in addition to the usually considered requirement of obstacle avoidance. In applications such as route planning, the use of dense properties is convenient as these describe the terrain and other aspects of the context of operation more rigorously and are usually the result of a concurrent mapping and learning process. Unfortunately, planning for a platform with more than three degrees of freedom can be computationally expensive, particularly if the application requires the platform to optimally deal with a thorough description of the terrain. The objective of this thesis is to develop and demonstrate an efficient path planning algorithm based on dynamic programming. The goal is to compute paths for ground vehicles with and without trailers, that minimise a specified cost-to-go while taking into account dynamic constraints of the vehicle and dense properties of the environment. The proposed approach utilises a Quadtree Piece-Wise Linear (QT-PWL) approximation to describe the environment in a low dimensional subspace and later uses a particle approach to introduce the dynamic constraints of the vehicle and to smooth the path in the full dimensional configuration space. This implies that the optimisation process can exploit the QT-PWL partition. Many usual contexts of operation of autonomous platforms have cluttered spaces and large regions where the dense properties are smooth; therefore, the QT-PWL partition is able to represent the context in a fraction of cells that would be needed by a homogeneous grid. The proposed methodology includes adaptations to both algorithms to achieve higher efficiency of the computational cost and optimality of the planned path. In order to demonstrate the capabilities of the algorithm, an idealized test case is presented and discussed. The case for a car and a tractor with multiple trailers is presented. A real path planning example is presented in addition to the synthetic experiments. Finally, the experiments and results are analysed and conclusions and directions for possible future work are presented