3,872 research outputs found
Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices
Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments;
where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range
estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in
delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both
ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient
signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution,
tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on
TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate
an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally,
we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible
acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance
LAVAPilot: Lightweight UAV Trajectory Planner with Situational Awareness for Embedded Autonomy to Track and Locate Radio-tags
Tracking and locating radio-tagged wildlife is a labor-intensive and
time-consuming task necessary in wildlife conservation. In this article, we
focus on the problem of achieving embedded autonomy for a resource-limited
aerial robot for the task capable of avoiding undesirable disturbances to
wildlife. We employ a lightweight sensor system capable of simultaneous (noisy)
measurements of radio signal strength information from multiple tags for
estimating object locations. We formulate a new lightweight task-based
trajectory planning method-LAVAPilot-with a greedy evaluation strategy and a
void functional formulation to achieve situational awareness to maintain a safe
distance from objects of interest. Conceptually, we embed our intuition of
moving closer to reduce the uncertainty of measurements into LAVAPilot instead
of employing a computationally intensive information gain based planning
strategy. We employ LAVAPilot and the sensor to build a lightweight aerial
robot platform with fully embedded autonomy for jointly tracking and planning
to track and locate multiple VHF radio collar tags used by conservation
biologists. Using extensive Monte Carlo simulation-based experiments,
implementations on a single board compute module, and field experiments using
an aerial robot platform with multiple VHF radio collar tags, we evaluate our
joint planning and tracking algorithms. Further, we compare our method with
other information-based planning methods with and without situational awareness
to demonstrate the effectiveness of our robot executing LAVAPilot. Our
experiments demonstrate that LAVAPilot significantly reduces (by 98.5%) the
computational cost of planning to enable real-time planning decisions whilst
achieving similar localization accuracy of objects compared to information gain
based planning methods, albeit taking a slightly longer time to complete a
mission.Comment: Accepted to 2020 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
Deep learning for internet of underwater things and ocean data analytics
The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes
Autonomous Vehicles
This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field
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