426 research outputs found
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Sampling-Based Exploration Strategies for Mobile Robot Autonomy
A novel, sampling-based exploration strategy is introduced for Unmanned Ground Vehicles (UGV) to efficiently map large GPS-deprived underground environments. It is compared to state-of-the-art approaches and performs on a similar level, while it is not designed for a specific robot or sensor configuration like the other approaches. The introduced exploration strategy, which is called Random-Sampling-Based Next-Best View Exploration (RNE), uses a Rapidly-exploring Random Graph (RRG) to find possible view points in an area around the robot. They are compared with a computation-efficient Sparse Ray Polling (SRP) in a voxel grid to find the next-best view for the exploration. Each node in the exploration graph built with RRG is evaluated regarding the ability of the UGV to traverse it, which is derived from an occupancy grid map. It is also used to create a topology-based graph where nodes are placed centrally to reduce the risk of collisions and increase the amount of observable space. Nodes that fall outside the local exploration area are stored in a global graph and are connected with a Traveling Salesman Problem solver to explore them later
Hybrid mapping for static and non-static indoor environments
Mención Internacional en el título de doctorIndoor environments populated by humans, such as houses, offices or universities,
involve a great complexity due to the diversity of geometries and situations that they
may present. Apart from the size of the environment, they can contain multiple rooms
distributed into floors and corridors, repetitive structures and loops, and they can
get as complicated as one can imagine. In addition, the structure and situations that
the environment present may vary over time as objects could be moved, doors can
be frequently opened or closed and places can be used for different purposes. Mobile
robots need to solve these challenging situations in order to successfully operate in
the environment. The main tools that a mobile robot has for dealing with these
situations relate to navigation and perception and comprise mapping, localization,
path planning and map adaptation. In this thesis, we try to address some of the open
problems in robot navigation in non-static indoor environments. We focus on house-like
environments as the work is framed into the HEROITEA research project that aims
attention at helping elderly people with their everyday-life activities at their homes.
This thesis contributes to HEROITEA with a complete robotic mapping system and
map adaptation that grants safe navigation and understanding of the environment.
Moreover, we provide localization and path planning strategies within the resulting
map to further operate in the environment.
The first problem tackled in this thesis is robot mapping in static indoor environments.
We propose a hybrid mapping method that structures the information gathered
from the environment into several maps. The hybrid map contains diverse knowledge of
the environment such as its structure, the navigable and blocked paths, and semantic
knowledge, such as the objects or scenes in the environment. All this information is
separated into different components of the hybrid map that are interconnected so the
system can, at any time, benefit from the information contained in every component.
In addition to the conceptual conception of the hybrid map, we have also developed
building procedures and an exploration algorithm to autonomous build the hybrid
map.
However, indoor environments populated by humans are far from being static as
the environment may change over time. For this reason, the second problem tackled in
this thesis is the adaptation of the map to non-static environments. We propose an
object-based probabilistic map adaptation that calculates the likelihood of moving or
remaining in its place for the different objects in the environment.
Finally, a map is just a description of the environment whose importance is mostly
related to how the map is used. In addition, map representations are more valuable
as long as they offer a wider range of applications. Therefore, the third problem
that we approach in this thesis is exploiting the intrinsic characteristics of the hybrid
map in order to enhance the performance of localization and path planning methods.
The particular objectives of these approaches are precision for robot localization and
efficiency for path planning in terms of execution time and traveled distance.
We evaluate our proposed methods in a diversity of simulated and real-world indoor
environments. In this extensive evaluation, we show that hybrid maps can be efficiently
built and maintained over time and they open up for new possibilities for localization
and path planning. In this thesis, we show an increase in localization precision and
robustness and an improvement in path planning performance.
In sum, this thesis makes several contributions in the context of robot navigation
in indoor environments, and especially in hybrid mapping. Hybrid maps offer higher
efficiency during map building and other applications such as localization and path
planning. In addition, we highlight the necessity of dealing with the dynamics of
indoor environments and the benefits of combining topological, semantic and metric
information to the autonomy of a mobile robot.Los entornos de interiores habitados por personas, como casas, oficinas o universidades,
entrañan una gran complejidad por la diversidad de geometrías y situaciones que pueden
ocurrir. Aparte de las diferencias en tamaño, estos entornos pueden contener muchas
habitaciones organizadas en diferentes plantas o pasillos, pueden presentar estructuras
repetitivas o bucles de tal forma que los entornos pueden llegar a ser tan complejos como
uno se pueda imaginar. Además, la estructura y el estado del entorno pueden variar
con el tiempo, ya que los objetos pueden moverse, las puertas pueden estar cerradas o
abiertas y diferentes espacios pueden ser usados para diferentes propósitos. Los robots
móviles necesitan resolver estas situaciones difíciles para poder funcionar de una forma
satisfactoria. Las principales herramientas que tiene un robot móvil para manejar
estas situaciones están relacionadas con la navegación y la percepción y comprenden el
mapeado, la localización, la planificación de trayectorias y la adaptación del mapa. En
esta tesis, abordamos algunos de los problemas sin resolver de la navegación de robots
móviles en entornos de interiores no estáticos. Nos centramos en entornos tipo casa ya
que este trabajo se enmarca en el proyecto de investigación HEROITEA que se enfoca
en ayudar a personas ancianas en tareas cotidianas del hogar. Esta tesis contribuye al
proyecto HEROITEA con un sistema completo de mapeado y adaptación del mapa
que asegura una navegación segura y la comprensión del entorno. Además, aportamos
métodos de localización y planificación de trayectorias usando el mapa construido para
realizar nuevas tareas en el entorno.
El primer problema que se aborda en esta tesis es el mapeado de entornos de
interiores estáticos por parte de un robot. Proponemos un método de mapeado híbrido
que estructura la información capturada en varios mapas. El mapa híbrido contiene
información sobre la estructura del entorno, las trayectorias libres y bloqueadas y
también incluye información semántica, como los objetos y escenas en el entorno. Toda
esta información está separada en diferentes componentes del mapa híbrido que están
interconectados de tal forma que el sistema puede beneficiarse en cualquier momento
de la información contenida en cada componente. Además de la definición conceptual del mapa híbrido, hemos desarrollado unos procedimientos para construir el mapa y un
algoritmo de exploración que permite que esta construcción se realice autónomamente.
Sin embargo, los entornos de interiores habitados por personas están lejos de ser
estáticos ya que pueden cambiar a lo largo del tiempo. Por esta razón, el segundo
problema que intentamos solucionar en esta tesis es la adaptación del mapa para
entornos no estáticos. Proponemos un método probabilístico de adaptación del mapa
basado en objetos que calcula la probabilidad de que cada objeto en el entorno haya
sido movido o permanezca en su posición anterior.
Para terminar, un mapa es simplemente una descripción del entorno cuya importancia
está principalmente relacionada con su uso. Por ello, los mapas más valiosos
serán los que ofrezcan un rango mayor de aplicaciones. Para abordar este asunto, el
tercer problema que intentamos solucionar es explotar las características intrínsecas del
mapa híbrido para mejorar el desempeño de métodos de localización y de planificación
de trayectorias usando el mapa híbrido. El objetivo principal de estos métodos es
aumentar la precisión en la localización del robot y la eficiencia en la planificación de
trayectorias en relación al tiempo de ejecución y la distancia recorrida.
Hemos evaluado los métodos propuestos en una variedad de entornos de interiores
simulados y reales. En esta extensa evaluación, mostramos que los mapas híbridos
pueden construirse y mantenerse en el tiempo de forma eficiente y que dan lugar a
nuevas posibilidades en cuanto a localización y planificación de trayectorias. En esta
tesis, mostramos un aumento en la precisión y robustez en la localización y una mejora
en el desempeño de la planificación de trayectorias.
En resumen, esta tesis lleva a cabo diversas contribuciones en el ámbito de la
navegación de robots móviles en entornos de interiores, y especialmente en mapeado
híbrido. Los mapas híbridos ofrecen más eficiencia durante la construcción del mapa
y en otras tareas como la localización y la planificación de trayectorias. Además,
resaltamos la necesidad de tratar los cambios en entornos de interiores y los beneficios
de combinar información topológica, semántica y métrica para la autonomía del robot.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Javier González Jiménez.- Vocal: Nancy Marie Amat
URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments
A major challenge with off-road autonomous navigation is the lack of maps or
road markings that can be used to plan a path for autonomous robots. Classical
path planning methods mostly assume a perfectly known environment without
accounting for the inherent perception and sensing uncertainty from detecting
terrain and obstacles in off-road environments. Recent work in computer vision
and deep neural networks has advanced the capability of terrain traversability
segmentation from raw images; however, the feasibility of using these noisy
segmentation maps for navigation and path planning has not been adequately
explored. To address this problem, this research proposes an uncertainty-aware
path planning method, URA* using aerial images for autonomous navigation in
off-road environments. An ensemble convolutional neural network (CNN) model is
first used to perform pixel-level traversability estimation from aerial images
of the region of interest. The traversability predictions are represented as a
grid of traversal probability values. An uncertainty-aware planner is then
applied to compute the best path from a start point to a goal point given these
noisy traversal probability estimates. The proposed planner also incorporates
replanning techniques to allow rapid replanning during online robot operation.
The proposed method is evaluated on the Massachusetts Road Dataset, the
DeepGlobe dataset, as well as a dataset of aerial images from off-road proving
grounds at Mississippi State University. Results show that the proposed image
segmentation and planning methods outperform conventional planning algorithms
in terms of the quality and feasibility of the initial path, as well as the
quality of replanned paths
SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments
Robotic exploration or monitoring missions require mobile robots to
autonomously and safely navigate between multiple target locations in
potentially challenging environments. Currently, this type of multi-goal
mission often relies on humans designing a set of actions for the robot to
follow in the form of a path or waypoints. In this work, we consider the
multi-goal problem of visiting a set of pre-defined targets, each of which
could be visited from multiple potential locations. To increase autonomy in
these missions, we propose a safe multi-goal (SMUG) planner that generates an
optimal motion path to visit those targets. To increase safety and efficiency,
we propose a hierarchical state validity checking scheme, which leverages
robot-specific traversability learned in simulation. We use LazyPRM* with an
informed sampler to accelerate collision-free path generation. Our iterative
dynamic programming algorithm enables the planner to generate a path visiting
more than ten targets within seconds. Moreover, the proposed hierarchical state
validity checking scheme reduces the planning time by 30% compared to pure
volumetric collision checking and increases safety by avoiding high-risk
regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its
capability to guide the robot in multi-goal missions fully autonomously on
rough terrain
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
System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams
NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots.
The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model.
A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern.
After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand.
This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals
How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
Estimating terrain traversability in off-road environments requires reasoning
about complex interaction dynamics between the robot and these terrains.
However, it is challenging to build an accurate physics model, or create
informative labels to learn a model in a supervised manner, for these
interactions. We propose a method that learns to predict traversability
costmaps by combining exteroceptive environmental information with
proprioceptive terrain interaction feedback in a self-supervised manner.
Additionally, we propose a novel way of incorporating robot velocity in the
costmap prediction pipeline. We validate our method in multiple short and
large-scale navigation tasks on a large, autonomous all-terrain vehicle (ATV)
on challenging off-road terrains, and demonstrate ease of integration on a
separate large ground robot. Our short-scale navigation results show that using
our learned costmaps leads to overall smoother navigation, and provides the
robot with a more fine-grained understanding of the interactions between the
robot and different terrain types, such as grass and gravel. Our large-scale
navigation trials show that we can reduce the number of interventions by up to
57% compared to an occupancy-based navigation baseline in challenging off-road
courses ranging from 400 m to 3150 m
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