564 research outputs found

    Airborne chemical sensing with mobile robots

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    Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

    Mobile Robots for Localizing Gas Emission Sources on Landfill Sites: Is Bio-Inspiration the Way to Go?

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    Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully “translated” into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms

    Environmental Monitoring using Autonomous Vehicles: A Survey of Recent Searching Techniques

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    Autonomous vehicles are becoming an essential tool in a wide range of environmental applications that include ambient data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. Among these applications, pollution source localization has drawn increasing interest due to its scientific and commercial interest and the emergence of a new breed of robotic vehicles capable of performing demanding tasks in harsh environments without human supervision. In this task, the aim is to find the location of a region that is the source of a given substance of interest (e.g. a chemical pollutant at sea or a gas leakage in air) using a group of cooperative autonomous vehicles. Motivated by fast paced advances in this challenging area, this paper surveys recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles

    Hazardous Chemical Source Localisation in Indoor Environments Using Plume-tracing Methods

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    Bio-inspired chemical plume-tracing methods have been applied to mobile robots to detect chemical emissions in the form of plumes and localise the plume sources in various indoor environments. Nevertheless, it has been found from the literature that most of the research has focused on plume tracing in free-stream plumes, such as indoor plumes where the chemical sources are located away from walls. Moreover, most of the experimental and numerical studies regarding the assessment of indoor plume-tracing algorithms have been undertaken in laboratory-scale environments. Since fluid fields and chemical concentration distributions of plumes near walls can be different from those of free-stream plumes, understanding of the performance of existing plume-tracing algorithms in near-wall regions is needed. In addition, the performance of different plume-tracing algorithms in detecting and tracing wall plumes in large-scale indoor environments is still unclear. In this research, a simulation framework combining ANSYS/FLUENT, which is used for simulating fluid fields and chemical concentration distributions of the environment, and MATLAB, with which plume-tracing algorithms are coded, is applied. In general, a plume-tracing algorithm can be divided into three stages: plume sensing, plume tracking and source localisation for analysis and discussion. In the first part of this research, an assessment of the performance of sixteen widely-used plume-tracing algorithms equipped with a concentration-distance obstacle avoidance method, was undertaken in two different scenarios. In one scenario, a single chemical source is located away from the walls in a wind-tunnel-like channel and in the other scenario, the chemical source is located near a wall. It is found that normal casting, surge anemotaxis and constant stepsize together performed the best, when compared with all the other algorithms. Also, the performance of the concentration-distance obstacle avoidance method is unsatisfactory. By applying an along-wall obstacle avoidance method, an algorithm called vallumtaxis, has been proposed and proved to contribute to higher efficiencies for plume tracing especially when searching in wall plumes. The results and discussion of the first part are presented in Chapter 4 of this thesis. In the second part, ten plume-tracing algorithms were tested and compared in four scenarios in a large-scale indoor environment: an underground warehouse. In these four scenarios, the sources are all on walls while their locations are different. The preliminary testing results of five algorithms show that for most failure cases, the robot failed at source localisation stage. Consequently, with different searching strategies at source localisation stage, this research investigated five further algorithms. The results demonstrated that the algorithm with a specially-designed pseudo casting source localisation method is the best approach to localising hazardous plume sources in the underground warehouse given in this research or other similar environments, among all the tested algorithms. The second part of the study is reported in Chapter 5 of this thesis.Thesis (MPhil) -- University of Adelaide, School of Mechanical Engineering, 202

    Oceanus.

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    v. 39, no. 1 (1996

    Underwater Vehicles

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    For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

    A Nature inspired guidance system for unmanned autonomous vehicles employed in a search role.

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    Since the very earliest days of the human race, people have been studying animal behaviours. In those early times, being able to predict animal behaviour gave hunters the advantages required for success. Then, as societies began to develop this gave way, to an extent, to agriculture and early studies, much of it trial and error, enabled farmers to successfully breed and raise livestock to feed an ever growing population. Following the advent of scientific endeavour, more rigorous academic research has taken human understanding of the natural world to much greater depth. In recent years, some of this understanding has been applied to the field of computing, creating the more specialised field of natural computing. In this arena, a considerable amount of research has been undertaken to exploit the analogy between, say, searching a given problem space for an optimal solution and the natural process of foraging for food. Such analogies have led to useful solutions in areas such as numerical optimisation and communication network management, prominent examples being ant colony systems and particle swarm optimisation; however, these solutions often rely on well-defined fitness landscapes that may not always be available. One practical application of natural computing may be to create behaviours for the control of autonomous vehicles that would utilise the findings of ethological research, identifying the natural world behaviours that have evolved over millennia to surmount many of the problems that autonomous vehicles find difficult; for example, long range underwater navigation or obstacle avoidance in fast moving environments. This thesis provides an exploratory investigation into the use of natural search strategies for improving the performance of autonomous vehicles operating in a search role. It begins with a survey of related work, including recent developments in autonomous vehicles and a ground breaking study of behaviours observed within the natural world that highlights general cooperative group behaviours, search strategies and communication methods that might be useful within a wider computing context beyond optimisation, where the information may be sparse but new paradigms could be developed that capitalise on research into biological systems that have developed over millennia within the natural world. Following this, using a 2-dimensional model, novel research is reported that explores whether autonomous vehicle search can be enhanced by applying natural search behaviours for a variety of search targets. Having identified useful search behaviours for detecting targets, it then considers scenarios where detection is lost and whether natural strategies for re-detection can improve overall systemic performance in search applications. Analysis of empirical results indicate that search strategies exploiting behaviours found in nature can improve performance over random search and commonly applied systematic searches, such as grids and spirals, across a variety of relative target speeds, from static targets to twice the speed of the searching vehicles, and against various target movement types such as deterministic movement, random walks and other nature inspired movement. It was found that strategies were most successful under similar target-vehicle relationships as were identified in nature. Experiments with target occlusion also reveal that natural reacquisition strategies could improve the probability oftarget redetection

    Mapping multiple gas/odor sources in an uncontrolled indoor environment using a Bayesian occupancy grid mapping based method

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    Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Robotics and Autonomous Systems 59 (2011): 988–1000, doi:10.1016/j.robot.2011.06.007.In this paper we address the problem of autonomously localizing multiple gas/odor sources in an indoor environment without a strong airflow. To do this, a robot iteratively creates an occupancy grid map. The produced map shows the probability each discrete cell contains a source. Our approach is based on a recent adaptation [15] to traditional Bayesian occupancy grid mapping for chemical source localization problems. The approach is less sensitive, in the considered scenario, to the choice of the algorithm parameters. We present experimental results with a robot in an indoor uncontrolled corridor in the presence of different ejecting sources proving the method is able to build reliable maps quickly (5.5 minutes in a 6 m x 2.1 m area) and in real time
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