FastM: Design and Evaluation of a Fast Mobility Mechanism for Wireless Mesh Networks

Although there is a large volume of work in the literature in terms of mobility approaches for Wireless Mesh Networks, usually these approaches introduce high latency in the handover process and do not support realtime services and applications. Moreover, mobility is decoupled from routing, which leads to inefficiency to both mobility and routing approaches with respect to mobility. In this paper we present a new extension to proactive routing protocols using a fast mobility extension, FastM, with the purpose of increasing handover performance in Wireless Mesh Networks. With this new extension, a new concept is created to integrate information between neighbor wireless mesh routers, managing locations of clients associated to wireless mesh routers in a certain neighborhood, and avoiding packet loss during handover. The proposed mobility approach is able to optimize the handover process without imposing any modifications to the current IEE 802.11 MAC protocol and use unmodified clients. Results show the improved efficiency of the proposed scheme: metrics such as disconnection time, throughput, packet loss and control overhead are largely improved when compared to previous approaches. Moreover, these conclusions apply to mobility scenarios, although mobility decreases the performance of the handover approach, as expected

분산 제약하에서 원격 제어되는 다수의 논홀로노믹 이동형 로봇 대형 재구성 제어

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 이동준.본 논문에서는 변화하는 주행 환경에서 분산 제약 하에 다수의 원격으로 제어되는 논홀로노믹 이동형 로봇 대형 재구성 제어에 대한 새로운 프레임워크를 제시하였다. 센싱과 컴퓨팅 능력이 갖추어진 온보드 시스템 로봇들을 활용하여 최근 개발된 예측 디스플레이 기법을 적용, 효율적인 군집 로봇의 원격 제어가 가능하도록 하였다. 잘 알려진 논홀로노믹 패시브 디컴포지션 기법을 기반으로 대형 변경이 가능하도록 새로운 파라미터를 추가, 대형 변경간 발생할 수 있는 문제들에 대해 파악하고 이를 해결하기 위해 포텐셜 필드를 활용하였다. n대의 로봇으로 다양한 대형 변경이 가능토록 시뮬레이션 환경을 조성, 39대의 탱크를 이용하여여 5가지의 각기 다른 대형으로의 변환을 새로이 제시한 알고리즘을 적용하여 구현하였다. 또한 실제 로봇 3대를 활용하여 알고리즘의 효용성에 대한 실험을 필두로 좁은 길목, 개활지 등 연속적으로 변화하는 환경 속에서의 구동을 통해 최종적으로 제시한 프레임워크의 타당성에 대해 검증하였다.We propose a novel framework for formation reconguration of multiple nonholonomic wheeled mobile robots (WMRs) in the changing driving environment. We utilize an onboard system of WMRs with the capability of sensing and computing. Each WMR has the same computing power for visualizing the driving environment, handling the sensing information and calculating the control action. One of the WMRs is the leader with the FPV camera and SLAM, while others with monocular cameras with limited FoV, as the followers, keep a certain desired formation during driving in a distributed manner. We set two control objectives, one is group driving and the other is holding the shape of the formation. We have to capture the control objectives separately and simultaneously, we make the best use of nonholonomic passive decomposition to split the WMRs' kinematics into those of the formation maintaining and group driving. The repulsive potential function to prevent the collision among WMRs and attractive potential function to restrict the boundary of follower WMRs' moving space due to limited FoV range of the monocular cameras while switching their formation are also used. Simulation with 39 tanks and experiments with three WMRs are also performed to verify the proposed framework.Acknowledgements iii List of Figures vii Abbreviations ix 1 Introduction 1 2 Formation Reconguration Control Design 5 2.1 Nonholonomic Passive Decomposition . . . . . . . . . . . . . . . 5 2.2 Attractive and Repulsive Potential Function . . . . . . . . . . . . 10 2.3 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3 Estimation and Predictive Display 20 3.1 Distributed Pose Estimation . . . . . . . . . . . . . . . . . . . . . 20 3.1.1 EKF Pose Estimation of Leader WMR . . . . . . . . . . . 20 3.1.2 EKF Pose Estimation of Follower WMRs . . . . . . . . . 22 3.2 Predictive Display for Distributed WMRs Teleoperation . . . . . 23 4 Experiment 27 4.1 Test Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2 Demonstrate the Proposed Algorithm . . . . . . . . . . . . . . . 30 4.3 Teleoperation Experiment with the Algorithm . . . . . . . . . . . 33 5 Conclusion 40Maste

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