Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

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

Modeling the Impact of Operator Trust on Performance in Multiple Robot Control,

We developed a system dynamics model to simulate the impact of operator trust on performance in multiple robot control. Analysis of a simulated urban search and rescue experiment showed that operators decided to manually control the robots when they lost trust in the autonomous planner that was directing the robots. Operators who rarely used manual control performed the worst. However, the operators who most frequently used manual control reported higher workload and did not perform any better than operators with moderate manual control usage. Based on these findings, we implemented a model where trust and performance form a feedback loop, in which operators perceive the performance of the system, calibrate their trust, and adjust their control of the robots. A second feedback loop incorporates the impact of trust on cognitive workload and system performance. The model was able to replicate the quantitative performance of three groups of operators within 2.3%. This model could help us gain a greater understanding of how operators build and lose trust in automation and the impact of those changes in trust on performance and workload, which is crucial to the development of future systems involving humanautomation collaboration.This research is sponsored by the Office of Naval Research and the Air Force Office of Scientific Research

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

Modeling the Impact of Operator Trust on Performance in Multiple Robot Control

We developed a system dynamics model to simulate the impact of operator trust on performance in multiple robot control. Analysis of a simulated urban search and rescue experiment showed that operators decided to manually control the robots when they lost trust in the autonomous planner that was directing the robots. Operators who rarely used manual control performed the worst. However, the operators who most frequently used manual control reported higher workload and did not perform any better than operators with moderate manual control usage. Based on these findings, we implemented a model where trust and performance form a feedback loop, in which operators perceive the performance of the system, calibrate their trust, and adjust their control of the robots. A second feedback loop incorporates the impact of trust on cognitive workload and system performance. The model was able to replicate the quantitative performance of three groups of operators within 2.3%. This model could help us gain a greater understanding of how operators build and lose trust in automation and the impact of those changes in trust on performance and workload, which is crucial to the development of future systems involving human-automation collaboration