6 research outputs found
Restorebot: Towards an Autonomous Robotics Platform for Degraded Rangeland Restoration
Degraded rangelands undergo continual shifts in the appearance and
distribution of plant life. The nature of these changes however is subtle:
between seasons seedlings sprout up and some flourish while others perish,
meanwhile, over multiple seasons they experience fluctuating precipitation
volumes and can be grazed by livestock. The nature of these conditioning
variables makes it difficult for ecologists to quantify the efficacy of
intervention techniques under study. To support these observation and
intervention tasks, we develop RestoreBot: a mobile robotic platform designed
for gathering data in degraded rangelands for the purpose of data collection
and intervention in order to support revegetation. Over the course of multiple
deployments, we outline the opportunities and challenges of autonomous data
collection for revegetation and the importance of further effort in this area.
Specifically, we identify that localization, mapping, data association, and
terrain assessment remain open problems for deployment, but that recent
advances in computer vision, sensing, and autonomy offer promising prospects
for autonomous revegetation.Comment: 12 pages, 14 figures, Accepted as a Contributed Paper at the 18th
International Symposium on Experimental Robotics (ISER 2023
BO-ICP: Initialization of Iterative Closest Point Based on Bayesian Optimization
Typical algorithms for point cloud registration such as Iterative Closest
Point (ICP) require a favorable initial transform estimate between two point
clouds in order to perform a successful registration. State-of-the-art methods
for choosing this starting condition rely on stochastic sampling or global
optimization techniques such as branch and bound. In this work, we present a
new method based on Bayesian optimization for finding the critical initial ICP
transform. We provide three different configurations for our method which
highlights the versatility of the algorithm to both find rapid results and
refine them in situations where more runtime is available such as offline map
building. Experiments are run on popular data sets and we show that our
approach outperforms state-of-the-art methods when given similar computation
time. Furthermore, it is compatible with other improvements to ICP, as it
focuses solely on the selection of an initial transform, a starting point for
all ICP-based methods.Comment: IEEE International Conference on Robotics and Automation 202
Tell Me Where to Go: A Composable Framework for Context-Aware Embodied Robot Navigation
Humans have the remarkable ability to navigate through unfamiliar
environments by solely relying on our prior knowledge and descriptions of the
environment. For robots to perform the same type of navigation, they need to be
able to associate natural language descriptions with their associated physical
environment with a limited amount of prior knowledge. Recently, Large Language
Models (LLMs) have been able to reason over billions of parameters and utilize
them in multi-modal chat-based natural language responses. However, LLMs lack
real-world awareness and their outputs are not always predictable. In this
work, we develop NavCon, a low-bandwidth framework that solves this lack of
real-world generalization by creating an intermediate layer between an LLM and
a robot navigation framework in the form of Python code. Our intermediate
shoehorns the vast prior knowledge inherent in an LLM model into a series of
input and output API instructions that a mobile robot can understand. We
evaluate our method across four different environments and command classes on a
mobile robot and highlight our NavCon's ability to interpret contextual
commands.Comment: 8 pages (24 with references and appendix), 6 Figure
Flexible Supervised Autonomy for Exploration in Subterranean Environments
While the capabilities of autonomous systems have been steadily improving in
recent years, these systems still struggle to rapidly explore previously
unknown environments without the aid of GPS-assisted navigation. The DARPA
Subterranean (SubT) Challenge aimed to fast track the development of autonomous
exploration systems by evaluating their performance in real-world underground
search-and-rescue scenarios. Subterranean environments present a plethora of
challenges for robotic systems, such as limited communications, complex
topology, visually-degraded sensing, and harsh terrain. The presented solution
enables long-term autonomy with minimal human supervision by combining a
powerful and independent single-agent autonomy stack, with higher level mission
management operating over a flexible mesh network. The autonomy suite deployed
on quadruped and wheeled robots was fully independent, freeing the human
supervision to loosely supervise the mission and make high-impact strategic
decisions. We also discuss lessons learned from fielding our system at the SubT
Final Event, relating to vehicle versatility, system adaptability, and
re-configurable communications.Comment: Field Robotics special issue: DARPA Subterranean Challenge,
Advancement and Lessons Learned from the Final
Present and Future of SLAM in Extreme Underground Environments
This paper reports on the state of the art in underground SLAM by discussing
different SLAM strategies and results across six teams that participated in the
three-year-long SubT competition. In particular, the paper has four main goals.
First, we review the algorithms, architectures, and systems adopted by the
teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to
approach for virtually all teams in the competition), heterogeneous multi-robot
operation (including both aerial and ground robots), and real-world underground
operation (from the presence of obscurants to the need to handle tight
computational constraints). We do not shy away from discussing the dirty
details behind the different SubT SLAM systems, which are often omitted from
technical papers. Second, we discuss the maturity of the field by highlighting
what is possible with the current SLAM systems and what we believe is within
reach with some good systems engineering. Third, we outline what we believe are
fundamental open problems, that are likely to require further research to break
through. Finally, we provide a list of open-source SLAM implementations and
datasets that have been produced during the SubT challenge and related efforts,
and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE
Transactions on Robotics for pre-approva
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Data for "Flexible Supervised Autonomy for Exploration in Subterranean Environments"
While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real- world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission, and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and reconfigurable communications.</p