Evaluation of Sensors and Mapping Approaches for Disasters in Tunnels

Ground or aerial robots equipped with advanced sensing technologies, such as three‐dimensional laser scanners and advanced mapping algorithms, are deemed useful as a supporting technology for first responders. A great deal of excellent research in the field exists, but practical applications at real disaster sites are scarce. Many projects concentrate on equipping robots with advanced capabilities, such as autonomous exploration or object manipulation. In spite of this, realistic application areas for such robots are limited to teleoperated reconnaissance or search. In this paper, we investigate how well state‐of‐the‐art and off‐the‐shelf components and algorithms are suited for reconnaissance in current disaster‐relief scenarios. The basic idea is to make use of some of the most common sensors and deploy some widely used algorithms in a disaster situation, and to evaluate how well the components work for these scenarios. We acquired the sensor data from two field experiments, one from a disaster‐relief operation in a motorway tunnel, and one from a mapping experiment in a partly closed down motorway tunnel. Based on these data, which we make publicly available, we evaluate state‐of‐the‐art and off‐the‐shelf mapping approaches. In our analysis, we integrate opinions and replies from first responders as well as from some algorithm developers on the usefulness of the data and the limitations of the deployed approaches, respectively. We discuss the lessons we learned during the two missions. These lessons are interesting for the community working in similar areas of urban search and rescue, particularly reconnaissance and search.     

Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center

The World Trade Center (WTC) rescue response provided an unfortunate opportunity to study the human-robot interactions (HRI) during a real unstaged rescue for the first time. A post-hoc analysis was performed on the data collected during the response, which resulted in 17 findings on the impact of the environment and conditions on the HRI: the skills displayed and needed by robots and humans, the details of the Urban Search and Rescue (USAR) task, the social informatics in the USAR domain, and what information is communicated at what time. The results of this work impact the field of robotics by providing a case study for HRI in USAR drawn from an unstaged USAR effort. Eleven recommendations are made based on the findings that impact the robotics, computer science, engineering, psychology, and rescue fields. These recommendations call for group organization and user confidence studies, more research into perceptual and assistive interfaces, and formal models of the state of the robot, state of the world, and information as to what has been observed.     

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

In this paper, we provide a review of the principal aspects related to search & rescue (SAR) with unmanned aerial vehicles (UAVs), with particular interest in the phase of post‐disaster assessment (PDA). Some areas of interest related to this topic have been chosen for the analysis: the aerial platforms used in the field, multirobot software architectures, onboard sensors and simultaneous localization and mapping approaches, terrain coverage algorithms, autonomous navigation techniques, and human‐swarm interfaces. All these aspects have been analyzed with respect to the state‐of‐the‐art, and also in relation to the project PRISMA, which focuses on the development and deployment of robots and autonomous systems that can operate in emergency scenarios, with a specific reference to monitoring and real‐time intervention.     

基于机器视觉的自主式救援机器人的研究

本文将面向自主式救援机器人,从现阶段日本、中国对自主式救援机器人研究现状出发,深入分析自主式救援机器人的功能需求和硬件设备需求,并且结合自主式救援机器人工作中所具备的功能进行研究。在灾害事故当中,由于环境复杂救援人员难以勘探真实的灾害环境,从而影响救援效果,而自主式救援机器人则能够通过自身强大的移动、远程通信、监测与跟踪等功能为救援人员提供帮助。面对灾害事故频发的现代化社会,救援机器人的研发已成为全球重要的研究事项。     

Path selection in disaster response management based on Q-learning

Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.     

Robotic Urban Search and Rescue: A Survey from the Control Perspective

Robotic urban search and rescue (USAR) is a challenging yet promising research area which has significant application potentials as has been seen during the rescue and recovery operations of recent disaster events. To date, the majority of rescue robots used in the field are teleoperated. In order to minimize a robot operator’s workload in time-critical disaster scenes, recent efforts have been made to equip these robots with some level of autonomy. This paper provides a detailed overview of developments in the exciting and challenging area of robotic control for USAR environments. In particular, we discuss the efforts that have been made in the literature towards: 1) developing low-level controllers for rescue robot autonomy in traversing uneven terrain and stairs, and perception-based simultaneous localization and mapping (SLAM) algorithms for developing 3D maps of USAR scenes, 2) task sharing of multiple tasks between operator and robot via semi-autonomous control, and 3) high-level control schemes that have been designed for multi-robot rescue teams.     

增强现实技术在防震减灾科普宣传中的应用

增强现实是将真实世界与虚拟世界结合在一起研究的新型领域,在众多行业、领域中有着广泛的应用,鉴于其优越的媒介传播价值,尝试将其应用于防震减灾科普宣传。基于对增强现实技术的基本概念、工作原理、关键技术和应用价值的介绍,分析防震减灾科普宣传的特点与目前存在的问题,浅析增强现实技术在防震减灾科普宣传应用中的优势。将增强现实技术应用于防震减灾科普宣传,能够实现便利的自订、自助功能,为相关科普宣传的趣味性、创新性、多元化发展提供助力。     

Piecewise‐planar Reconstruction of Multi‐room Interiors with Arbitrary Wall Arrangements

Reconstructing the as‐built architectural shape of building interiors has emerged in recent years as an important and challenging research problem. An effective approach must be able to faithfully capture the architectural structures and separate permanent components from clutter (e.g. furniture), while at the same time dealing with defects in the input data. For many applications, higher‐level information on the environment is also required, in particular the shape of individual rooms. To solve this ill‐posed problem, state‐of‐the‐art methods assume constrained input environments with a 2.5D or, more restrictively, a Manhattan‐world structure, which significantly restricts their applicability in real‐world settings. We present a novel pipeline that allows to reconstruct general 3D interior architectures, significantly increasing the range of real‐world architectures that can be reconstructed and labeled by any interior reconstruction method to date. Our method finds candidate permanent components by reasoning on a graph‐based scene representation, then uses them to build a 3D linear cell complex that is partitioned into separate rooms through a multi‐label energy minimization formulation. We demonstrate the effectiveness of our method by applying it to a variety of real‐world and synthetic datasets and by comparing it to more specialized state‐of‐the‐art approaches.     

增强现实综述

增强现实(augmented reality，AR)技术可以将虚拟的物体合并到现实场景中，并能支持用户与其进行交互，它已经成为虚拟现实研究中的一个重要领域，也是人机界面技术发展的一个重要方向。为了使人们对其有所了解，该文首先概略描述了这个领域的主要研究内容和进展情况，并详细介绍了增强现实中的支撑技术、开发工具和相关理论；然后针对当前AR应用的现状，分析了实现中的难点问题，并给出了与AR普及应用密切相关的一些系统框架和开发平台的描述，最后介绍了几个典型的AR应用实例。     

Exploiting Spatial Locality and Heterogeneity of Agents for Search and Rescue Teamwork*

We propose coordination mechanisms for multiple heterogeneous physical agents that operate in city‐scale disaster scenarios, where they need to find and rescue people and extinguish fires. Large‐scale disasters are characterized by limited and unreliable communications; dangerous events that may disable agents; uncertainty about the location, duration, and type of tasks; and stringent temporal constraints on task completion times. In our approach, agents form teams with other agents that are in the same geographical area. Our algorithms either yield stable teams formed up front and never change, fluid teams where agents can change teams as need arises, or teams that restrict the types of agents that can belong to the same team. We compare our teaming algorithms against a baseline algorithm in which agents operate independently of others and two state‐of‐the‐art coordination mechanisms. Our algorithms are tested in city‐scale disaster simulations using the RoboCup Rescue simulator. Our experiments with different city maps show that, in general, forming teams leads to increased task completion and, specifically, that our teaming method that restricts the types of agents in a team outperforms the other methods.     

On the predictability of domain‐independent temporal planners

Temporal planning is a research discipline that addresses the problem of generating a totally or a partially ordered sequence of actions that transform the environment from some initial state to a desired goal state, while taking into account time constraints and actions' duration. For its ability to describe and address temporal constraints, temporal planning is of critical importance for a wide range of real‐world applications. Predicting the performance of temporal planners can lead to significant improvements in the area, as planners can then be combined in order to boost the performance on a given set of problem instances. This paper investigates the predictability of the state‐of‐the‐art temporal planners by introducing a new set of temporal‐specific features and exploiting them for generating classification and regression empirical performance models (EPMs) of considered planners. EPMs are also tested with regard to their ability to select the most promising planner for efficiently solving a given temporal planning problem. Our extensive empirical analysis indicates that the introduced set of features allows to generate EPMs that can effectively perform algorithm selection, and the use of EPMs is therefore a promising direction for improving the state of the art of temporal planning, hence fostering the use of planning in real‐world applications.     

Improving the coordination efficiency of limited‐communication multi–autonomus underwater vehicle operations using a multiagent architecture

This research addresses the problem of coordinating multiple autonomous underwater vehicle (AUV) operations. An intelligent mission executive has been created that uses multiagent technology to control and coordinate multiple AUVs in communication‐deficient environments. By incorporating real‐time vehicle prediction, blackboard‐based hierarchical mission plans, mission optimization, and a distributed multiagent–based paradigm in conjunction with a simple broadcast communication system, this research aims to handle the limitations inherent in underwater operations, namely poor communication, and intelligently control multiple vehicles. In this research, efficiency is evaluated and then compared to the current state of the art in multiple AUV control. The research is then validated in real AUV coordination trials. Results will show that compared to the state of the art, the control system developed and implemented in this research coordinates multiple vehicles more efficiently and is able to function in a range of poor communication environments. These findings are supported by in‐water validation trials with heterogeneous AUVs. © 2010 Wiley Periodicals, Inc.     

Editorial of special issue on unmanned aerial vehicles (UAVs)

UAVs have witnessed unprecedented levels of growth during the last decade. Projections and predictions suggest that during the next 5-10 years growth will continue to rapidly increase, while the spectrum of UAV utilization will be dominated by civil and public domain applications, ranging from search and rescue, emergency response, disaster management, infrastructure monitoring and protection, precision agriculture, surveillance and reconnaissance, cartography, etc. This special issue on UAVs consists of six invited and peer reviewed papers. The main focus of the issue is on multi-UAV teams, a research area that has attracted attention due to the fact that a team of unmanned vehicles may accomplish, collectively, tasks that may be difficult or impossible by a single UAV to complete. The first four papers focus on: Flocking control of a fleet of UAVs; distributed output feedback stationary consensus of multi-vehicle systems in unknown environments; consensus controller for multi-UAV navigation; and ranging-aided relative navigation of multi-platforms. Collectively, these four papers offer insight to the state of the art in this important topic. The fifth paper on nonlinear robust control of a quadrotor helicopter with finite time convergence addresses challenges related to UAV navigation/control, while the last paper on experimental evaluation of a real-time GPU-based pose estimation system for autonomous landing of rotary wing UAVs introduces a comprehensive methodology that is suitable for real-time autonomous takeoff and landing from stationary and moving ground platforms.     

Calibration of a magnetometer array using motion capture equipment

This paper studies the problem of calibrating an array of single‐axis magnetometers in an unknown static inhomogeneous magnetic field using motion capture equipment. A proof of identifiability is given, practical identifiability of calibration parameters is established in simulation, and real world experiments are conducted to demonstrate the feasibility of this approach. Unlike many state of the art techniques, the proposed solution does not require a homogeneous field, as in fact, we demonstrate that an inhomogeneous field enlarges the set of identifiable parameters. Under the above‐mentioned assumptions, this approach may be used to extend self‐calibration techniques of visual‐inertial setups to magnetic sensor arrays in indoor environments.     

Many Plans: Multidimensional Ensembles for Visual Decision Support in Flood Management

Uncertainties in flood predictions complicate the planning of mitigation measures. There is a consensus that many possible incident scenarios should be considered. For each scenario, a specific response plan should be prepared which is optimal with respect to criteria such as protection, costs, or realization time. None of the existing software tools is capable of creating large scenario pools, nor do they provide means for quick exploration and assessment of the associated plans. In this paper, we present an integrated solution that is based on multidimensional, time‐dependent ensemble simulations of incident scenarios and protective measures. We provide scalable interfaces which facilitate and accelerate setting up multiple time‐varying parameters for generating a pool of pre‐cooked scenarios. In case of an emergency, disaster managers can quickly extract relevant information from the pool to deal with the situation at hand. An interactive 3D‐view conveys details about how a response plan has to be executed. Linked information visualization and ranking views allow for a quick assessment of many plans. In collaboration with flood managers, we demonstrate the practical applicability of our solution. We tackle the challenges of planning mobile water barriers for protecting important infrastructure. We account for real‐world limitations of available resources and handle the involved logistics problems.     

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

Real‐world systems change continuously. In domains such as traffic monitoring or cyber security, such changes occur within short time scales. This results in a streaming data problem and leads to unique challenges for the human in the loop, as analysts have to ingest and make sense of dynamic patterns in real time. While visualizations are being increasingly used by analysts to derive insights from streaming data, we lack a thorough characterization of the human‐centred design problems and a critical analysis of the state‐of‐the‐art solutions that exist for addressing these problems. In this paper, our goal is to fill this gap by studying how the state of the art in streaming data visualization handles the challenges and reflect on the gaps and opportunities. To this end, we have three contributions in this paper: (i) problem characterization for identifying domain‐specific goals and challenges for handling streaming data, (ii) a survey and analysis of the state of the art in streaming data visualization research with a focus on how visualization design meets challenges specific to change perception and (iii) reflections on the design trade‐offs, and an outline of potential research directions for addressing the gaps in the state of the art.     

增强现实技术综述

增强现实技术是将计算机渲染生成的虚拟场景与真实世界中的场景无缝融合起来的一种技术,它通过视频显示设备将虚实融合的场景呈现给用户,使人们与计算机之间的交互更加的自然,同时具有广泛的应用前景,因此成为近年来的一个研究热点。随着跟踪注册技术的进步、计算机性能的飞速发展、深度摄像机的普及,以及Light Field投影技术在增强现实中的应用,增强现实技术逐渐成为下一代人机交互的发展方向。该文章首先概述了增强现实的主要研究内容和发展情况,并详细介绍了增强现实的关键技术、开发工具,然后分类概述了增强现实应用案例。     

The State of the Art in Multilayer Network Visualization

Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.     

BRDF Representation and Acquisition

Photorealistic rendering of real world environments is important in a range of different areas; including Visual Special effects, Interior/Exterior Modelling, Architectural Modelling, Cultural Heritage, Computer Games and Automotive Design. Currently, rendering systems are able to produce photorealistic simulations of the appearance of many real‐world materials. In the real world, viewer perception of objects depends on the lighting and object/material/surface characteristics, the way a surface interacts with the light and on how the light is reflected, scattered, absorbed by the surface and the impact these characteristics have on material appearance. In order to re‐produce this, it is necessary to understand how materials interact with light. Thus the representation and acquisition of material models has become such an active research area. This survey of the state‐of‐the‐art of BRDF Representation and Acquisition presents an overview of BRDF (Bidirectional Reflectance Distribution Function) models used to represent surface/material reflection characteristics, and describes current acquisition methods for the capture and rendering of photorealistic materials.     

Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

Existing artificial intelligence solutions typically operate in powerful platforms with high computational resources availability. However, a growing number of emerging use cases such as those based on unmanned aerial systems (UAS) require new solutions with embedded artificial intelligence on a highly mobile platform. This paper proposes an innovative UAS that explores machine learning (ML) capabilities in a smartphone‐based mobile platform for object detection and recognition applications. A new system framework tailored to this challenging use case is designed with a customized workflow specified. Furthermore, the design of the embedded ML leverages TensorFlow, a cutting‐edge open‐source ML framework. The prototype of the system integrates all the architectural components in a fully functional system, and it is suitable for real‐world operational environments such as seek and rescue use cases. Experimental results validate the design and prototyping of the system and demonstrate an overall improved performance compared with the state of the art in terms of a wide range of metrics.