375 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
Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
In outdoor environments, mobile robots are required to navigate through
terrain with varying characteristics, some of which might significantly affect
the integrity of the platform. Ideally, the robot should be able to identify
areas that are safe for navigation based on its own percepts about the
environment while avoiding damage to itself. Bayesian optimisation (BO) has
been successfully applied to the task of learning a model of terrain
traversability while guiding the robot through more traversable areas. An
issue, however, is that localisation uncertainty can end up guiding the robot
to unsafe areas and distort the model being learnt. In this paper, we address
this problem and present a novel method that allows BO to consider localisation
uncertainty by applying a Gaussian process model for uncertain inputs as a
prior. We evaluate the proposed method in simulation and in experiments with a
real robot navigating over rough terrain and compare it against standard BO
methods.Comment: To appear in the proceedings of the 18th International Symposium on
Robotics Research (ISRR 2017
Towards an Autonomous Walking Robot for Planetary Surfaces
In this paper, recent progress in the development of
the DLR Crawler - a six-legged, actively compliant walking
robot prototype - is presented. The robot implements
a walking layer with a simple tripod and a more complex
biologically inspired gait. Using a variety of proprioceptive
sensors, different reflexes for reactively crossing obstacles
within the walking height are realised. On top of
the walking layer, a navigation layer provides the ability
to autonomously navigate to a predefined goal point in
unknown rough terrain using a stereo camera. A model
of the environment is created, the terrain traversability is
estimated and an optimal path is planned. The difficulty
of the path can be influenced by behavioral parameters.
Motion commands are sent to the walking layer and the
gait pattern is switched according to the estimated terrain
difficulty. The interaction between walking layer and navigation
layer was tested in different experimental setups
Reinforcement and Curriculum Learning for Off-Road Navigation of an UGV with a 3D LiDAR
This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation
of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection
and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator
Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an ActorâCritic Neural
Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the
3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability
scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated
experiments and favorably compared with a previous reactive navigation approach on the same UGV.Partial funding for open access charge: Universidad de MĂĄlag
Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments
A key challenge in off-road navigation is that even visually similar terrains
or ones from the same semantic class may have substantially different traction
properties. Existing work typically assumes no wheel slip or uses the expected
traction for motion planning, where the predicted trajectories provide a poor
indication of the actual performance if the terrain traction has high
uncertainty. In contrast, this work proposes to analyze terrain traversability
with the empirical distribution of traction parameters in unicycle dynamics,
which can be learned by a neural network in a self-supervised fashion. The
probabilistic traction model leads to two risk-aware cost formulations that
account for the worst-case expected cost and traction. To help the learned
model generalize to unseen environment, terrains with features that lead to
unreliable predictions are detected via a density estimator fit to the trained
network's latent space and avoided via auxiliary penalties during planning.
Simulation results demonstrate that the proposed approach outperforms existing
work that assumes no slip or uses the expected traction in both navigation
success rate and completion time. Furthermore, avoiding terrains with low
density-based confidence score achieves up to 30% improvement in success rate
when the learned traction model is used in a novel environment.Comment: To appear in IROS23. Video and code:
https://github.com/mit-acl/mppi_numb
Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments
Discriminating the traversability of terrains is a crucial task for
autonomous driving in off-road environments. However, it is challenging due to
the diverse, ambiguous, and platform-specific nature of off-road
traversability. In this paper, we propose a novel self-supervised terrain
traversability learning framework, utilizing a contrastive label disambiguation
mechanism. Firstly, weakly labeled training samples with pseudo labels are
automatically generated by projecting actual driving experiences onto the
terrain models constructed in real time. Subsequently, a prototype-based
contrastive representation learning method is designed to learn distinguishable
embeddings, facilitating the self-supervised updating of those pseudo labels.
As the iterative interaction between representation learning and pseudo label
updating, the ambiguities in those pseudo labels are gradually eliminated,
enabling the learning of platform-specific and task-specific traversability
without any human-provided annotations. Experimental results on the RELLIS-3D
dataset and our Gobi Desert driving dataset demonstrate the effectiveness of
the proposed method.Comment: 9 pages, 11 figure
Traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data
Scene perception and traversability analysis are real challenges for autonomous driving systems. In the context of off-road autonomy, there are additional challenges due to the unstructured environments and the existence of various vegetation types. It is necessary for the Autonomous Ground Vehicles (AGVs) to be able to identify obstacles and load-bearing surfaces in the terrain to ensure a safe navigation (McDaniel et al. 2012). The presence of vegetation in off-road autonomy applications presents unique challenges for scene understanding: 1) understory vegetation makes it difficult to detect obstacles or to identify load-bearing surfaces; and 2) trees are usually regarded as obstacles even though only trunks of the trees pose collision risk in navigation. The overarching goal of this dissertation was to study traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data. More specifically, to address the aforementioned challenges, this dissertation studied the impacts of the understory vegetation density on the solid obstacle detection performance of the off-road autonomous systems. By leveraging a physics-based autonomous driving simulator, a classification-based machine learning framework was proposed for obstacle detection based on point cloud data captured by LIDAR. Features were extracted based on a cumulative approach meaning that information related to each feature was updated at each timeframe when new data was collected by LIDAR. It was concluded that the increase in the density of understory vegetation adversely affected the classification performance in correctly detecting solid obstacles. Additionally, a regression-based framework was proposed for estimating the understory vegetation density for safe path planning purposes according to which the traversabilty risk level was regarded as a function of estimated density. Thus, the denser the predicted density of an area, the higher the risk of collision if the AGV traversed through that area. Finally, for the trees in the terrain, the dissertation investigated statistical features that can be used in machine learning algorithms to differentiate trees from solid obstacles in the context of forested off-road scenes. Using the proposed extracted features, the classification algorithm was able to generate high precision results for differentiating trees from solid obstacles. Such differentiation can result in more optimized path planning in off-road applications
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
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