830 research outputs found
Wireless Sensor Networks for Underwater Localization: A Survey
Autonomous Underwater Vehicles (AUVs) have widely deployed in marine investigation and ocean exploration in recent years. As the fundamental information, their position information is not only for data validity but also for many real-world applications. Therefore, it is critical for the AUV to have the underwater localization capability. This report is mainly devoted to outline the recent advance- ment of Wireless Sensor Networks (WSN) based underwater localization. Several classic architectures designed for Underwater Acoustic Sensor Network (UASN) are brie y introduced. Acoustic propa- gation and channel models are described and several ranging techniques are then explained. Many state-of-the-art underwater localization algorithms are introduced, followed by the outline of some existing underwater localization systems
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment
The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology. An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization
An Overview of AUV Algorithms Research and Testbed at the University of Michigan
This paper provides a general overview of the autonomous underwater vehicle (AUV) research projects being pursued within the Perceptual Robotics Laboratory (PeRL) at the University of Michigan. Founded in 2007, PeRL's research thrust is centered around improving AUV autonomy via algorithmic advancements in sensor-driven perceptual feedback for environmentally-based real-time mapping, navigation, and control. In this paper we discuss our three major research areas of: (1) real-time visual simultaneous localization and mapping (SLAM); (2) cooperative multi-vehicle navigation; and (3) perception-driven control. Pursuant to these research objectives, PeRL has acquired and significantly modified two commercial off-the-shelf (COTS) Ocean-Server Technology, Inc. Iver2 AUV platforms to serve as a real-world engineering testbed for algorithm development and validation. Details of the design modification, and related research enabled by this integration effort, are discussed herein.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86058/1/reustice-15.pd
Model-Based Underwater 6D Pose Estimation from RGB
Object pose estimation underwater allows an autonomous system to perform
tracking and intervention tasks. Nonetheless, underwater target pose estimation
is remarkably challenging due to, among many factors, limited visibility, light
scattering, cluttered environments, and constantly varying water conditions. An
approach is to employ sonar or laser sensing to acquire 3D data, but besides
being costly, the resulting data is normally noisy. For this reason, the
community has focused on extracting pose estimates from RGB input. However, the
literature is scarce and exhibits low detection accuracy. In this work, we
propose an approach consisting of a 2D object detection and a 6D pose
estimation that reliably obtains object poses in different underwater
scenarios. To test our pipeline, we collect and make available a dataset of 4
objects in 10 different real scenes with annotations for object detection and
pose estimation. We test our proposal in real and synthetic settings and
compare its performance with similar end-to-end methodologies for 6D object
pose estimation. Our dataset contains some challenging objects with symmetrical
shapes and poor texture. Regardless of such object characteristics, our
proposed method outperforms stat-of-the-art pose accuracy by ~8%. We finally
demonstrate the reliability of our pose estimation pipeline by doing
experiments with an underwater manipulation in a reaching task.Comment: Under RA-L Submissio
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