493 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
A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights
This article provides a detailed analysis of the assessment of unmanned ground vehicle terrain traversability. The analysis is categorized into terrain classification, terrain mapping, and cost-based traversability, with subcategories of appearance-based, geometry-based, and mixed-based methods. The article also explores the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) and other based end-to-end methods as crucial components for advanced terrain traversability analysis. The investigation indicates that a mixed approach, incorporating both exteroceptive and proprioceptive sensors, is more effective, optimized, and reliable for traversability analysis. Additionally, the article discusses the vehicle platforms and sensor technologies used in traversability analysis, making it a valuable resource for researchers in the field. Overall, this paper contributes significantly to the current understanding of traversability analysis in unstructured environments and provides insights for future sensor-based research on advanced traversability analysis
AdVENTR: Autonomous Robot Navigation in Complex Outdoor Environments
We present a novel system, AdVENTR for autonomous robot navigation in
unstructured outdoor environments that consist of uneven and vegetated
terrains. Our approach is general and can enable both wheeled and legged robots
to handle outdoor terrain complexity including unevenness, surface properties
like poor traction, granularity, obstacle stiffness, etc. We use data from
sensors including RGB cameras, 3D Lidar, IMU, robot odometry, and pose
information with efficient learning-based perception and planning algorithms
that can execute on edge computing hardware. Our system uses a scene-aware
switching method to perceive the environment for navigation at any time instant
and dynamically switches between multiple perception algorithms. We test our
system in a variety of sloped, rocky, muddy, and densely vegetated terrains and
demonstrate its performance on Husky and Spot robots
Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles
Due to the energy-consumption efficiency between up-slope and down-slope is
hugely different, a path with the shortest length on a complex off-road terrain
environment (2.5D map) is not always the path with the least energy
consumption. For any energy-sensitive vehicles, realizing a good trade-off
between distance and energy consumption on 2.5D path planning is significantly
meaningful. In this paper, a deep reinforcement learning-based 2.5D
multi-objective path planning method (DMOP) is proposed. The DMOP can
efficiently find the desired path with three steps: (1) Transform the
high-resolution 2.5D map into a small-size map. (2) Use a trained deep Q
network (DQN) to find the desired path on the small-size map. (3) Build the
planned path to the original high-resolution map using a path enhanced method.
In addition, the imitation learning method and reward shaping theory are
applied to train the DQN. The reward function is constructed with the
information of terrain, distance, border. Simulation shows that the proposed
method can finish the multi-objective 2.5D path planning task. Also, simulation
proves that the method has powerful reasoning capability that enables it to
perform arbitrary untrained planning tasks on the same map
Path Generation for Wheeled Robots Autonomous Navigation on Vegetated Terrain
Wheeled robot navigation has been widely used in urban environments, but
little research has been conducted on its navigation in wild vegetation.
External sensors (LiDAR, camera etc.) are often used to construct point cloud
map of the surrounding environment, however, the supporting rigid ground used
for travelling cannot be detected due to the occlusion of vegetation. This
often causes unsafe or not smooth path during planning process. To address the
drawback, we propose the PE-RRT* algorithm, which effectively combines a novel
support plane estimation method and sampling algorithm to generate real-time
feasible and safe path in vegetation environments. In order to accurately
estimate the support plane, we combine external perception and proprioception,
and use Multivariate Gaussian Processe Regression (MV-GPR) to estimate the
terrain at the sampling nodes. We build a physical experimental platform and
conduct experiments in different outdoor environments. Experimental results
show that our method has high safety, robustness and generalization
Legged Robots for Object Manipulation: A Review
Legged robots can have a unique role in manipulating objects in dynamic,
human-centric, or otherwise inaccessible environments. Although most legged
robotics research to date typically focuses on traversing these challenging
environments, many legged platform demonstrations have also included "moving an
object" as a way of doing tangible work. Legged robots can be designed to
manipulate a particular type of object (e.g., a cardboard box, a soccer ball,
or a larger piece of furniture), by themselves or collaboratively. The
objective of this review is to collect and learn from these examples, to both
organize the work done so far in the community and highlight interesting open
avenues for future work. This review categorizes existing works into four main
manipulation methods: object interactions without grasping, manipulation with
walking legs, dedicated non-locomotive arms, and legged teams. Each method has
different design and autonomy features, which are illustrated by available
examples in the literature. Based on a few simplifying assumptions, we further
provide quantitative comparisons for the range of possible relative sizes of
the manipulated object with respect to the robot. Taken together, these
examples suggest new directions for research in legged robot manipulation, such
as multifunctional limbs, terrain modeling, or learning-based control, to
support a number of new deployments in challenging indoor/outdoor scenarios in
warehouses/construction sites, preserved natural areas, and especially for home
robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical
Engineerin
Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain
Most autonomous navigation systems assume wheeled robots are rigid bodies and
their 2D planar workspaces can be divided into free spaces and obstacles.
However, recent wheeled mobility research, showing that wheeled platforms have
the potential of moving over vertically challenging terrain (e.g., rocky
outcroppings, rugged boulders, and fallen tree trunks), invalidate both
assumptions. Navigating off-road vehicle chassis with long suspension travel
and low tire pressure in places where the boundary between obstacles and free
spaces is blurry requires precise 3D modeling of the interaction between the
chassis and the terrain, which is complicated by suspension and tire
deformation, varying tire-terrain friction, vehicle weight distribution and
momentum, etc. In this paper, we present a learning approach to model wheeled
mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan
feasible, stable, and efficient motion to drive over vertically challenging
terrain without rolling over or getting stuck. We present physical experiments
on two wheeled robots and show that planning using our learned model can
achieve up to 60% improvement in navigation success rate and 46% reduction in
unstable chassis roll and pitch angles.Comment: https://www.youtube.com/watch?v=VzpRoEZeyWk
https://cs.gmu.edu/~xiao/Research/Verti-Wheelers
Inclined Surface Locomotion Strategies for Spherical Tensegrity Robots
This paper presents a new teleoperated spherical tensegrity robot capable of
performing locomotion on steep inclined surfaces. With a novel control scheme
centered around the simultaneous actuation of multiple cables, the robot
demonstrates robust climbing on inclined surfaces in hardware experiments and
speeds significantly faster than previous spherical tensegrity models. This
robot is an improvement over other iterations in the TT-series and the first
tensegrity to achieve reliable locomotion on inclined surfaces of up to
24\degree. We analyze locomotion in simulation and hardware under single and
multi-cable actuation, and introduce two novel multi-cable actuation policies,
suited for steep incline climbing and speed, respectively. We propose
compelling justifications for the increased dynamic ability of the robot and
motivate development of optimization algorithms able to take advantage of the
robot's increased control authority.Comment: 6 pages, 11 figures, IROS 201
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