2,297 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
Intelligent Behavior of Autonomous Vehicles in Outdoor Environment
The objective of this PhD-project has been to develop and enhance the operational behaviour of autonomous or automated conventional machines under out-door conditions. This has included developing high-level planning measures for the maximisation of machine productivity as an important element in the continued efforts of planning and controlling resource inputs in both arable and high value crops farming. The methods developed generate the optimized coverage path for any field regardless of its complexity on 2D or 3D terrains without any human intervention and in a manner that minimizes operational time, skipped and overlapped areas, and fuel consumption. By applying the developed approaches, a reduction of more than 20% in consumed fossil fuel together with a corresponding reduction in the emissions of CO2 and other greenhouses is achievable.In this work, a software package for the autonomous navigation of field robotics over 2D and 3D field terrains and the optimization of field operations and machinery systems have been developed. A web-based version of the developed software package is currently under progress
Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains
This paper presents a safe, efficient, and agile ground vehicle navigation
algorithm for 3D off-road terrain environments. Off-road navigation is subject
to uncertain vehicle-terrain interactions caused by different terrain
conditions on top of 3D terrain topology. The existing works are limited to
adopt overly simplified vehicle-terrain models. The proposed algorithm learns
the terrain-induced uncertainties from driving data and encodes the learned
uncertainty distribution into the traversability cost for path evaluation. The
navigation path is then designed to optimize the uncertainty-aware
traversability cost, resulting in a safe and agile vehicle maneuver. Assuring
real-time execution, the algorithm is further implemented within parallel
computation architecture running on Graphics Processing Units (GPU).Comment: 6 pages, 6 figures, submitted to International Conference on Robotics
and Automation (ICRA 2023
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