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

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated

A mobile anchor assisted localization algorithm based on regular hexagon in wireless sensor networks

Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints of cost and power consumption make it infeasible to equip each sensor node in the network with a global position system(GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use several mobile anchors which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. This paper proposes a mobile anchor assisted localization algorithm based on regular hexagon (MAALRH) in two-dimensional WSNs, which can cover the whole monitoring area with a boundary compensation method. Unknown nodes calculate their positions by using trilateration. We compare the MAALRH with HILBERT, CIRCLES, and S-CURVES algorithms in terms of localization ratio, localization accuracy, and path length. Simulations show that the MAALRH can achieve high localization ratio and localization accuracy when the communication range is not smaller than the trajectory resolution.The work is supported by the Natural Science Foundation of Jiangsu Province of China, no. BK20131137; the Applied Basic Research Program of Nantong Science and Technology Bureau, no. BK2013032; and the Guangdong University of Petrochemical Technology's Internal Project, no. 2012RC0106. Jaime Lloret's work has been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516. Joel J. P. C. 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