8,776 research outputs found
Robust Localization and Efficient Path Planning for Mobile Sensor Networks
학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 오성회.The area of wireless sensor networks has flourished over the past decade
due to advances in micro-electro-mechanical sensors,
low power communication and computing protocols, and embedded microprocessors.
Recently, there has been a growing interest in mobile sensor networks,
along with the development of robotics,
and mobile sensor networks have enabled networked sensing system
to solve the challenging issues of wireless sensor networks by adding mobility into many different applications of wireless sensor networks.
Nonetheless, there are many challenges to be
addressed in mobile sensor networks.
Among these, the estimation for the exact location is perhaps
the most important to obtain high fidelity of the sensory information.
Moreover, planning should be required to send the mobile sensors
to sensing location considering the region of interest, prior to sensor placements.
These are the fundamental problems in realizing mobile sensor networks
which is capable of performing monitoring mission in unstructured and dynamic environment.
In this dissertation, we take an advantage of mobility
which mobile sensor networks possess
and develop localization and path planning algorithms
suitable for mobile sensor networks.
We also design coverage control strategy using resource-constrained mobile sensors
by taking advantages of the proposed path planning method.
The dissertation starts with the localization problem,
one of the fundamental issue in mobile sensor networks.
Although global positioning system (GPS) can perform
relatively accurate localization,
it is not feasible in many situations, especially indoor environment
and costs a tremendous amount in deploying all robots
equipped with GPS sensors.
Thus we develop the indoor localization system suitable
for mobile sensor networks using inexpensive robot platform.
We focus on the technique that relies primarily on the camera sensor.
Since it costs less than other sensors,
all mobile robots can be easily equipped with cameras.
In this dissertation, we demonstrate that the proposed method is
suitable for mobile sensor networks requiring an inexpensive off-the-shelf
robotic platform, by showing that it provides consistently
robust location information for low-cost noisy sensors.
We also focus on another fundamental issue of mobile sensor networks
which is a path planning problem in order to deploy
mobile sensors in specific locations.
Unlike the traditional planning methods,
we present an efficient cost-aware planning method suitable for mobile sensor networks
by considering the given environment,
where it has environmental parameters such as
temperature, humidity, chemical concentration, stealthiness and elevation.
A global stochastic optimization method is used to improve
the efficiency of the sampling based planning algorithm.
This dissertation presents the first approach of
sampling based planning using global tree extension.
Based on the proposed planning method,
we also presents a general framework for modeling a coverage control system
consisting of multiple robots with resource constraints
suitable for mobile sensor networks.
We describe the optimal informative planning methods
which deal with maximization problem with constraints
using global stochastic optimization method.
In addition, we describe how to find trajectories
for multiple robots efficiently to estimate the environmental field
using information obtained from all robots.Chapter 1 Introduction 1
1.1 Mobile Sensor networks 1
1.1.1 Challenges 3
1.2 Overview of the Dissertation 4
Chapter 2 Background 7
2.1 Localization in MSNs 7
2.2 Path planning in MSNs 10
2.3 Informative path planning in MSNs 12
Chapter 3 Robust Indoor Localization 15
3.1 An Overview of Coordinated Multi-Robot Localization 16
3.2 Multi-Robot Localization using Multi-View Geometry 19
3.2.1 Planar Homography for Robot Localization 20
3.2.2 Image Based Robot Control 21
3.3 Multi-Robot Navigation System 25
3.3.1 Multi-Robot System 26
3.3.2 Multi-Robot Navigation 30
3.4 Experimental Results 32
3.4.1 Coordinated Multi-Robot Localization: Single-Step 32
3.4.2 Coordinated Multi-Robot Localization: Multi-Step 36
3.5 Discussions and Comparison to Leap-Frog 42
3.5.1 Discussions 42
3.5.2 Comparison to Leap-Frog 45
3.6 Summary 51
Chapter 4 Preliminaries to Cost-Aware Path Planning 53
4.1 Related works 54
4.2 Sampling based path planning 56
4.3 Cross entropy method 59
4.3.1 Cross entropy based path planning 63
Chapter 5 Fast Cost-Aware Path Planning using Stochastic Optimization 65
5.1 Problem formulation 66
5.2 Issues with sampling-based path planning for complex terrains or high dimensional spaces 68
5.3 Cost-Aware path planning (CAPP) 73
5.3.1 CE Extend 75
5.4 Analysis of CAPP 81
5.4.1 Probabilistic Completeness 81
5.4.2 Asymptotic optimality 83
5.5 Simulation and experimental results 84
5.5.1 (P1) Cost-Aware Navigation in 2D 85
5.5.2 (P2) Complex Terrain Navigation 88
5.5.3 (P3) Humanoid Motion Planning 96
5.6 Summary 103
Chapter 6 Effcient Informative Path Planning 105
6.1 Problem formulation 106
6.2 Cost-Aware informative path planning (CAIPP) 109
6.2.1 Overall procedure 110
6.2.2 Update Bound 112
6.2.3 CE Estimate 115
6.3 Analysis of CAIPP 118
6.4 Simulation and experimental results 120
6.4.1 Single robot informative path planning 120
6.4.2 Multi robot informative path planning 122
6.5 Summary 125
Chapter 7 Conclusion and Future Work 129
Appendices 131
Appendix A Proof of Theorem 1 133
Appendix B Proof of Theorem 2 135
Appendix C Proof of Theorem 3 137
Appendix D Proof of Theorem 4 139
Appendix E Dubins' curve 141
Bibliography 147
초 록 163Docto
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
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
Special issue on smart interactions in cyber-physical systems: Humans, agents, robots, machines, and sensors
In recent years, there has been increasing interaction between humans and non‐human systems as we move further beyond the industrial age, the information age, and as we move into the fourth‐generation society. The ability to distinguish between human and non‐human capabilities has become more difficult to discern. Given this, it is common that cyber‐physical systems (CPSs) are rapidly integrated with human functionality, and humans have become increasingly dependent on CPSs to perform their daily routines.The constant indicators of a future where human and non‐human CPSs relationships consistently interact and where they allow each other to navigate through a set of non‐trivial goals is an interesting and rich area of research, discovery, and practical work area. The evidence of con- vergence has rapidly gained clarity, demonstrating that we can use complex combinations of sensors, artificial intelli- gence, and data to augment human life and knowledge. To expand the knowledge in this area, we should explain how to model, design, validate, implement, and experiment with these complex systems of interaction, communication, and networking, which will be developed and explored in this special issue. This special issue will include ideas of the future that are relevant for understanding, discerning, and developing the relationship between humans and non‐ human CPSs as well as the practical nature of systems that facilitate the integration between humans, agents, robots, machines, and sensors (HARMS).Fil: Kim, Donghan. Kyung Hee University;Fil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Matson, Eric T.. Purdue University; Estados UnidosFil: Kim, Gerard Jounghyun. Korea University
Human-Machine Interface for Remote Training of Robot Tasks
Regardless of their industrial or research application, the streamlining of
robot operations is limited by the proximity of experienced users to the actual
hardware. Be it massive open online robotics courses, crowd-sourcing of robot
task training, or remote research on massive robot farms for machine learning,
the need to create an apt remote Human-Machine Interface is quite prevalent.
The paper at hand proposes a novel solution to the programming/training of
remote robots employing an intuitive and accurate user-interface which offers
all the benefits of working with real robots without imposing delays and
inefficiency. The system includes: a vision-based 3D hand detection and gesture
recognition subsystem, a simulated digital twin of a robot as visual feedback,
and the "remote" robot learning/executing trajectories using dynamic motion
primitives. Our results indicate that the system is a promising solution to the
problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and
Techniques - IST201
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization,
mapping and navigation. These sensors, however, cannot detect transparent
surfaces or measure the full occupancy of complex objects such as tables. Deep
Neural Networks have recently been proposed to overcome this limitation by
learning to estimate object occupancy. These estimates are nevertheless subject
to uncertainty, making the evaluation of their confidence an important issue
for these measures to be useful for autonomous navigation and mapping. In this
work we approach the problem from two sides. First we discuss uncertainty
estimation in deep models, proposing a solution based on a fully convolutional
neural network. The proposed architecture is not restricted by the assumption
that the uncertainty follows a Gaussian model, as in the case of many popular
solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout.
We present results showing that uncertainty over obstacle distances is actually
better modeled with a Laplace distribution. Then, we propose a novel approach
to build maps based on Deep Neural Network uncertainty models. In particular,
we present an algorithm to build a map that includes information over obstacle
distance estimates while taking into account the level of uncertainty in each
estimate. We show how the constructed map can be used to increase global
navigation safety by planning trajectories which avoid areas of high
uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference
on Humanoid Robots (Humanoids)
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