A team of mobile robots and monitoring sensors—from concept to experiment

This paper describes the underlying concepts, architecture and implementation of a robotic system consisting of heterogenous mobile robots and stationary sensors, cooperating in a task of collective perception and world modeling. The navigation capability of a group of robots can be improved by sharing available information about the state of the environment (the environment model) and information about the relative position estimates. The information sharing can be especially beneficial to the robots when there are also some stationary monitoring sensors (e.g. cameras) available in the environment, which can serve as external navigation aids. In the article, information processing performed by individual members of the team—robots and sensors—is analyzed and a unifying multi-agent blackboard architecture is described. For information sharing between robots and monitoring sensors, a framework based on the idea of the Contract Net Protocol is proposed. The communication backbone provides agents with unified communication interfaces. The experimental set-up is described. The results of tests validating the correctness of the design on the tasks of cooperative localization and world-model building are reported. A discussion and comparison to other multi-robot systems closes the article.     

Optimal spatial formation of swarm robotic gas sensors in odor plume finding

Finding the best spatial formation of stationary gas sensors in detection of odor clues is the first step of searching for olfactory targets in a given space using a swarm of robots. Considering no movement for a network of gas sensors, this paper formulates the problem of odor plume detection and analytically finds the optimal spatial configuration of the sensors for plume detection, given a set of assumptions. This solution was analyzed and verified by simulations and finally experimentally validated in a reduced scale realistic environment using a set of Roomba-based mobile robots.     

Selected hormonal and immunological responses to strenuous live-fire firefighting drills

This study investigated the effects of strenuous live-fire firefighting drills and a 90 min recovery period on selected hormonal, immunological and psychological variables. Apparently healthy, male, professional firefighters (n = 11) performed three trials of standardized firefighting tasks in a live-fire training structure. There was significant leukocytosis immediately post firefighting activity that persisted following recovery, although there was a variable response among the leukocyte subsets. Most notable was the decrease in number and percentage of lymphocytes following 90 min of recovery. Plasma levels of ACTH and cortisol were significantly elevated post firefighting activity and cortisol remained elevated following 90 min of recovery. Elevated cortisol immediately following activity was related to reduced feelings of energy. These data demonstrate the magnitude of the physiological and psychological disruption following strenuous firefighting activity and suggest that immune function may be altered following such activity. This is a finding that may have practical consequences for this group of first responders.     

Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes

When an emergency occurs within a building, it may be initially safer to send autonomous mobile nodes, instead of human responders, to explore the area and identify hazards and victims. Exploring all the area in the minimum amount of time and reporting back interesting findings to the human personnel outside the building is an essential part of rescue operations. Our assumptions are that the area map is unknown, there is no existing network infrastructure, long-range wireless communication is unreliable and nodes are not location-aware. We take into account these limitations, and propose an architecture consisting of both mobile nodes (robots, called agents) and stationary nodes (inexpensive smart devices, called tags). As agents enter the emergency area, they sprinkle tags within the space to label the environment with states. By reading and updating the state of the local tags, agents are able to coordinate indirectly with each other, without relying on direct agent-to-agent communication. In addition, tags wirelessly exchange local information with nearby tags to further assist agents in their exploration task. Our simulation results show that the proposed algorithm, which exploits both tag-to-tag and agent-to-tag communication, outperforms previous algorithms that rely only on agent-to-tag communication.     

Motion-based identification of multiple mobile robots using trajectory analysis in a well-configured environment with distributed vision sensors

Networked mobile robots are able to determine their poses (i.e., position and orientation) with the help of a well-configured environment with distributed sensors. Before localizing the mobile robots using distributed sensors, the environment has to have information on each of the robots?? prior knowledge. Consequently, if the environment does not have information on the prior knowledge of a certain mobile robot then it will not determine its current pose. To solve this restriction, as a preprocessing step for indoor localization, we propose a motion-based identification of multiple mobile robots using trajectory analysis. The proposed system identifies the robots by establishing the relation between their identities and their positions, which are estimated from their trajectories related to each of the paths generated as designated signs. The primary feature of the proposed system is the fact that networked mobile robots are quickly and simultaneously able to determine their poses in well-configured environments. Experimental results show that our proposed system simultaneously identifies multiple mobile robots, and approximately estimates each of their poses as an initial state for autonomous localization.     

模糊神经网络在移动机器人信息融合中的应用

针对移动机器人所用的传感器,提出了一种用于多传感器信息融合的方法,将模糊逻辑和神经网络结合起来,构建了模糊神经网络,并建立了网络的计算模型.通过建立的模糊神经网络对移动机器人的多传感器信息进行融合,实现了移动机器人对动态环境中障碍和环境类型的实时识别以及无冲突运动.网络的训练和试验表明该方法在移动机器人躲避运动物体中是可行的.     

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels.     

Mobile computing in urban emergency situations: Improving the support to firefighters in the field

Communication support is a serious limitation for Latin American firefighters when they deal with emergency situations. The insufficient number of radio channels and the impossibility to deliver digital information force firemen to improvise during response processes, e.g., to make decisions using their experience and poor or null supporting information. These improvised actions affect the time required to take control of an emergency, and also affect the evolution of the crisis situation. Provided most of Latin American fire companies are volunteer organizations, communication solutions that could help to overcome these problems are usually expensive for them. This article presents a low-cost mobile collaborative application, which may be used in emergency situations to overcome most of the firefighters’ communication problems. The application, named MobileMap, is the result of the research and development work conducted by the authors, supported by a Chilean fire company, during the last three years. MobileMap allows ad hoc communication, decisions support and collaboration among firefighters in the field using mobile devices. This solution complements the radio communication systems. Since the interactions supported by MobileMap are recorded, it is possible to analyze such information after the crisis and learn for future emergencies. The tool was evaluated in simulated and real scenarios, and the obtained results are highly encouraging.     

Comparison of the effect of typical firefighting activities,live fire drills and rescue operations at height on firefighters’ physiological responses and cognitive function

Abstract

This study examined the impact of various types of firefighting activities on firefighters’ physiological responses and cognitive function. Each firefighter was engaged in three conditions: (1) Live-fire activities (LFA), (2) Typical firefighting activities (TFA), and (3) Rescue operations at height (ROH). The effects of various types of firefighting activities on the physiological responses and cognitive function were evaluated by heart rate (HR), temporal artery temperature (TT), and the correct response (CR) on a cognitive test. The results indicated that, compared to the baseline, physiological response increased, while information processing performance decreased after the activity. Furthermore, HR and TT were significantly lower at the end of the firefighting activity in the LFA (149.33 bpm; 38.08?°C) compared with the TFA (152.22 bpm; 38.17?°C) and ROH (159.28 bpm; 38.24?°C) conditions. Also, CR was significantly higher at the end of the activity in the LFA and TFA compared with the ROH condition. The results showed that rescue at height was more intensive than the other firefighting tasks in decreasing physiological and cognitive function capacity after the experiment.

Practitioner Summary: We assumed that various types of firefighting activities would have different effects on physiological and cognitive functions during firefighting activities. The Findings suggest that rescue at height operations, performed without the use of special protective equipment, was more influential than other firefighting duties in changing firefighters’ physiological and cognitive capacity.

Abbreviations: CR: correct response; LFA: live-fire activities; TFA: typical firefighting activities; ROH: rescue operations at height; HR: heart rate; TT: temporal artery tempearture; PASAT: pased auditory serila addition teat; FPC: firefighting protective clothing     

全区域覆盖移动机器人无标识工作环境的边界建立与识别

针对工作在户外环境中的全区域覆盖移动机器人,提出在无人为标识的工作环境中,建立边界和识别边界的新方案. 设计组合传感器定位系统实现工作区域的边界建立和识别. 基于分段策略建立工作边界地图,并对其性能与连续获取策略进行比较分析. 设计RBF神经网络以实时获取精确的定位信息. 实验证明, 定位系统和方案能够保证工作区域边界的准确建立和成功识别,机器人能顺利完成全区域覆盖任务,而且具有一定的工程实用性.     

A geometric approach to deploying robot swarms

We discuss the fundamental problems and practical issues underlying the deployment of a swarm of autonomous mobile robots that can potentially be used to build mobile robotic sensor networks. For the purpose, a geometric approach is proposed that allows robots to configure themselves into a two-dimensional plane with uniform spatial density. Particular emphasis is paid to the hole repair capability for dynamic network reconfiguration. Specifically, each robot interacts selectively with two neighboring robots so that three robots can converge onto each vertex of the equilateral triangle configuration. Based on the local interaction, the self-configuration algorithm is presented to enable a swarm of robots to form a communication network arranged in equilateral triangular lattices by shuffling the neighbors. Convergence of the algorithms is mathematically proved using Lyapunov theory. Moreover, it is verified that the self-reparation algorithm enables robot swarms to reconfigure themselves when holes exist in the network or new robots are added to the network. Through extensive simulations, we validate the feasibility of applying the proposed algorithms to self-configuring a network of mobile robotic sensors. We describe in detail the features of the algorithm, including self-organization, self-stabilization, and robustness, with the results of the simulation.     

A nature-inspired decentralized trust model to reduce information unreliability in complex disaster relief operations

One of the foremost challenges confronting society today is the vulnerability of critical physical infrastructures in urban areas to extreme events. The major challenge in these scenarios despite the availability of the means of communication (e.g. ad-hoc networks, hand held devices) is the lack of trust among the first responders due to their having no prior experience of interactions amongst themselves. Also the spreading of inaccurate or misleading information can have disastrous consequences in the chaotic and hazardous environments which are prevalent in large-scale disasters. Therefore it is imperative that the responders possess the ability to assess and evaluate the trustworthiness as well as the information propagated by fellow responders in order to facilitate collaboration. This paper presents a decentralized trust model to enhance reliable information dissemination in large-scale disasters. The model proposed includes a distributed recommendation scheme, incorporated into an existing membership maintenance service for ad-hoc networks. In addition it has a nature-inspired activation spreading mechanism that allows trust-based information propagation. Search and Rescue exercises involving civil engineers and firefighters were conducted at the Illinois Fire Service Institute (IFSI) to test the software. The upshot of the simulation was immediate and robust establishment of trust and high resilience to the spread of the unreliable information.     

Tracking Targets Using Multiple Robots: The Effect of Environment Occlusion

This paper addresses the problem of tracking multiple targets using a network of communicating robots and stationary sensors. We introduce a Region-based Approach which controls robot deployment at two levels. A coarse deployment controller distributes robots across regions using a topological map which maintains urgency estimates for each region, and a target-following controller attempts to maximize the number of tracked targets within a region. A behavior-based system is presented implementing the Region-Based Approach, which is fully distributed and scalable. We compared the Region-based Approach to a 'naive' local-following strategy in three environments with varying degree of occlusion. The experimental results showed that the Region-based Approach performs better than the naive strategy when the environment has significant occlusion. Second, we performed experiments (the environment was held constant) in which two techniques for computing urgency estimates were compared. Last, different combinations of mobile sensors and stationary sensors were compared in a given environment.     

Perception network for the team of indoor mobile robots: concept,architecture, implementation

The concept of perception network with application to the distributed perception processes taking place among mobile robots operating on the shared shop-floor is discussed. Its relationship with the distributed environment modeling is pointed out. The concept of geometrical database is combined with multiple classes of maps generated with particular physical sensors, in order to obtain the world model. The logical and functional structure of the perception network has been proposed to reflect the semantics of the transportation system consisting of the team of indoor mobile robots.     

An ontology to represent firefighters data requirements during building fire emergencies

Firefighters require accurate and timely information regarding a building and its environment to perform their duty safely and successfully during a fire emergency. However, due to the chaotic nature of building fires, firefighters often receive erroneous, conflicting, or delayed information that can affect the outcome of an emergency. In this paper, we propose a solution in the form of an ontology that defines building and environmental data that is needed by firefighters during a building fire emergency. The ontology development followed the METHONTOLOGY method and was implemented using the web ontology language (OWL) in Protégé 5.5.0. Built-in reasoners in Protégé and an ontology pitfall scanning tool were employed to verify the structure and consistency of the new ontology. To validate the ontology efficacy, we developed a prototype web application to represent and visualise relevant information based on the ontology and used that as a basis for conducting interviews with firefighters. Finally, a specification document that describes the ontology was created and published online. The proposed ontology can be a basis for developing intelligent tools and systems that support firefighters.     

Fuzzy target tracking control of autonomous mobile robots by using infrared sensors

The theme of this paper is to design a real-time fuzzy target tracking control scheme for autonomous mobile robots by using infrared sensors. At first two mobile robots are setup in the target tracking problem, where one is the target mobile robot with infrared transmitters and the other one is the tracker mobile robot with infrared receivers and reflective sensors. The former is designed to drive in a specific trajectory. The latter is designed to track the target mobile robot. Then we address the design of the fuzzy target tracking control unit, which consists of a behavior network and a gate network. The behavior network possesses the fuzzy wall following control (FWFC) mode, fuzzy target tracking control (FTTC) mode, and two fixed control modes to deal with different situations in real applications. Both the FWFC and FTTC are realized by the fuzzy sliding-mode control scheme. A gate network is used to address the fusion of measurements of two infrared sensors and is developed to recognize which situation is belonged to and which action should be executed. Moreover, the target tracking control with obstacle avoidance is also investigated in this paper. Both computer simulations and real-time implementation experiments of autonomous target tracking control demonstrate the effectiveness and feasibility of the proposed control schemes.     

移动机器人非视觉传感器及其信号处理方法

非视觉传感器是机器人认识和了解外部环境的重要途径，移动机器人常用的非视觉 传感器包括超声、红外、接近传感器等．这些传感器大多是以环或阵列的形式出现，因此其 信号处理往往要占用机器人大量的CPU时间．本文提出了一种采用多DSP控制和处理各类非视 觉传感器的方法，给出了传感器信号处理的原理和具体实现．同时我们引入了并行处理的机 制，各类传感器信号处理可同时进行，在很大程度上提高了机器人传感器信号处理的速度， 有利于机器人在实时动态环境中运行．并给出了非视觉传感器信号处理的实验结果，验证了 该方法的有效性．      

Kinematic modeling of wheeled mobile robots

We formulate the kinematic equations of motion of wheeled mobile robots incorporating conventional, omnidirectional, and ball wheels.¹ We extend the kinematic modeling of stationary manipulators to accommodate such special characteristics of wheeled mobile robots as multiple closed-link chains, higher-pair contact points between a wheel and a surface, and unactuated and unsensed wheel degrees of freedom. We apply the Sheth-Uicker convention to assign coordinate axes and develop a matrix coordinate transformation algebra to derive the equations of motion. We introduce a wheel Jacobian matrix to relate the motions of each wheel to the motions of the robot. We then combine the individual wheel equations to obtain the composite robot equation of motion. We interpret the properties of the composite robot equation to characterize the mobility of a wheeled mobile robot according to a mobility characterization tree. Similarly, we apply actuation and sensing characterization trees to delineate the robot motions producible by the wheel actuators and discernible by the wheel sensors, respectively. We calculate the sensed forward and actuated inverse solutions and interpret the physical conditions which guarantee their existence. To illustrate the development, we formulate and interpret the kinematic equations of motion of Uranus, a wheeled mobile robot being constructed in the CMU Mobile Robot Laboratory.     

Designing dependable agent systems for mobile wireless networks

A mobile ad hoc network (MANET) is a wireless network of mobile devices-such as PDAs, laptops, cell phones, and other lightweight, easily transportable computing devices-in which each node can act as a router for network traffic rather than relying on fixed networking infrastructure. As mobile computing becomes ubiquitous, MANETS becomes increasingly important. As a design paradigm, multiagent systems (MASs) can help facilitate and coordinate ad hoc-scenarios that might include security personnel, rescue workers, police officers, firefighters, and paramedics. On this network, mobile agents perform critical functions that include delivering messages, monitoring resource usage on constrained mobile devices, assessing network traffic patterns, analyzing host behaviors, and revoking access rights for suspicious hosts and agents. Agents can effectively operate in such environments if they are environment aware - if they can sense and reason about their complex and dynamic environments. Altogether, agents living on a MANET must be network, information, and performance aware. This article fleshes out how we apply this approach to populations of mobile agents on a live MANET.     

传感器网络中利用反演集合估计的机器人定位方法

针对大型协作环境中移动机器人的全局定位问题,提出根据机器人车载传感器、环境传感器以及其他机器人的实时数据估计移动机器人的位置。首先,提出的方法整合大量不同类型传感器,从最简单传感器到最复杂传感器;然后,考虑了测量值数量可变、通用测角测量、受容错约束的测量统计知识等约束条件,将非线性边界误差估计问题看作一种反演集合。最后处理特定类型的异常值和不精确环境下的模型误差。完成了误差和异常值的处理,就基本上获得了定位图,解决了移动机器人的定位问题。提出的方法利用实物实验进行验证。测试区域装备有多个传感器、固定在墙顶部的摄像机以及位于机器人上的可见标记。实验结果表明提出的方法在协作环境中具有明显优势,处理异常值更加可靠。