3,009 research outputs found
Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails
Robots hold promise in many scenarios involving outdoor use, such as
search-and-rescue, wildlife management, and collecting data to improve
environment, climate, and weather forecasting. However, autonomous navigation
of outdoor trails remains a challenging problem. Recent work has sought to
address this issue using deep learning. Although this approach has achieved
state-of-the-art results, the deep learning paradigm may be limited due to a
reliance on large amounts of annotated training data. Collecting and curating
training datasets may not be feasible or practical in many situations,
especially as trail conditions may change due to seasonal weather variations,
storms, and natural erosion. In this paper, we explore an approach to address
this issue through virtual-to-real-world transfer learning using a variety of
deep learning models trained to classify the direction of a trail in an image.
Our approach utilizes synthetic data gathered from virtual environments for
model training, bypassing the need to collect a large amount of real images of
the outdoors. We validate our approach in three main ways. First, we
demonstrate that our models achieve classification accuracies upwards of 95% on
our synthetic data set. Next, we utilize our classification models in the
control system of a simulated robot to demonstrate feasibility. Finally, we
evaluate our models on real-world trail data and demonstrate the potential of
virtual-to-real-world transfer learning.Comment: iROS 201
Adaptive and intelligent navigation of autonomous planetary rovers - A survey
The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations
The goal of the Mars Sample Return campaign is to collect soil samples from
the surface of Mars and return them to Earth for further study. The samples
will be acquired and stored in metal tubes by the Perseverance rover and
deposited on the Martian surface. As part of this campaign, it is expected that
the Sample Fetch Rover will be in charge of localizing and gathering up to 35
sample tubes over 150 Martian sols. Autonomous capabilities are critical for
the success of the overall campaign and for the Sample Fetch Rover in
particular. This work proposes a novel system architecture for the autonomous
detection and pose estimation of the sample tubes. For the detection stage, a
Deep Neural Network and transfer learning from a synthetic dataset are
proposed. The dataset is created from photorealistic 3D simulations of Martian
scenarios. Additionally, the sample tubes poses are estimated using Computer
Vision techniques such as contour detection and line fitting on the detected
area. Finally, laboratory tests of the Sample Localization procedure are
performed using the ExoMars Testing Rover on a Mars-like testbed. These tests
validate the proposed approach in different hardware architectures, providing
promising results related to the sample detection and pose estimation.Comment: Preprint version only. Final version at IEEE Xplore. Accepted for
IEEE Robotics and Automation Letter
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