127 research outputs found

    Tracking Odor Plumes in a Laminar Wind Field with Bio-Inspired Algorithms

    Get PDF
    We introduce a novel bio-inspired odor source localization algorithm (surge- cast) for environments with a main wind ïŹ‚ow and compare it to two well-known algorithms. With all three algorithms, systematic experiments with real robots are carried out in a wind tunnel under laminar ïŹ‚ow conditions. The algorithms are compared in terms of distance overhead when tracking the plume up to the source, but a variety of other experimental results and some theoretical considerations are provided as well. We conclude that the surge-cast algorithm yields signiïŹcantly better performance than the casting algorithm, and slightly better performance than the surge-spiral algorithm

    An evaluation of plume tracking as a strategy for gas source localization in turbulent wind flows

    Get PDF
    Gas source localization is likely the most direct application of a mobile robot endowed with gas sensing capabilities. Multiple algorithms have been proposed to locate the gas source within a known environment, ranging from bio-inspired to probabilistic ones. However, their application to real-world conditions still remains a major issue due to the great difficulties those scenarios bring, among others, the common presence of obstacles which hamper the movement of the robot and notably ncrease the complexity of the gas dispersion. In this work, we consider a plume tracking algorithm based on the well-known silkworm moth strategy and analyze its performance when facing different realistic environments characterized by the presence of obstacles and turbulent wind flows. We rely on computational fluid dynamics and the open source gas dispersion simulator GADEN to generate realistic gas distributions in scenarios where the presence of obstacles breaks down the ideal downwind plume. We first propose some modifications to the original silkworm moth algorithm in order to deal with the presence of obstacles in the environment (avoiding collisions) and then analyze its performance within four challenging environments.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂ­a Tech. Proyecto de excelencia de la Junata de Andalucia TEP2012-53

    Robotic Olfactory-Based Navigation with Mobile Robots

    Get PDF
    Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. It has been viewed as challenging due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to correctly finding an odor source is designing an effective olfactory-based navigation algorithm, which guides the robot to detect emitted odor plumes as cues in finding the source. This dissertation proposes three kinds of olfactory-based navigation methods to improve search efficiency while maintaining a low computational cost, incorporating different machine learning and artificial intelligence methods. A. Adaptive Bio-inspired Navigation via Fuzzy Inference Systems. In nature, animals use olfaction to perform many life-essential activities, such as homing, foraging, mate-seeking, and evading predators. Inspired by the mate-seeking behaviors of male moths, this method presents a behavior-based navigation algorithm for using on a mobile robot to locate an odor source. Unlike traditional bio-inspired methods, which use fixed parameters to formulate robot search trajectories, a fuzzy inference system is designed to perceive the environment and adjust trajectory parameters based on the current search situation. The robot can automatically adapt the scale of search trajectories to fit environmental changes and balance the exploration and exploitation of the search. B. Olfactory-based Navigation via Model-based Reinforcement Learning Methods. This method analogizes the odor source localization as a reinforcement learning problem. During the odor plume tracing process, the belief state in a partially observable Markov decision process model is adapted to generate a source probability map that estimates possible odor source locations. A hidden Markov model is employed to produce a plume distribution map that premises plume propagation areas. Both source and plume estimates are fed to the robot. A decision-making model based on a fuzzy inference system is designed to dynamically fuse information from two maps and balance the exploitation and exploration of the search. After assigning the fused information to reward functions, a value iteration-based path planning algorithm solves the optimal action policy. C. Robotic Odor Source Localization via Deep Learning-based Methods. This method investigates the viability of implementing deep learning algorithms to solve the odor source localization problem. The primary objective is to obtain a deep learning model that guides a mobile robot to find an odor source without explicating search strategies. To achieve this goal, two kinds of deep learning models, including adaptive neuro-fuzzy inference system (ANFIS) and deep neural networks (DNNs), are employed to generate the olfactory-based navigation strategies. Multiple training data sets are acquired by applying two traditional methods in both simulation and on-vehicle tests to train deep learning models. After the supervised training, the deep learning models are verified with unseen search situations in simulation and real-world environments. All proposed algorithms are implemented in simulation and on-vehicle tests to verify their effectiveness. Compared to traditional methods, experiment results show that the proposed algorithms outperform them in terms of the success rate and average search time. Finally, the future research directions are presented at the end of the dissertation

    Towards Odor-Sensitive Mobile Robots

    Get PDF
    J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 VersiĂłn preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

    Tracking an Odor Plume in a Laminar Wind Field with the Crosswind-Surge Algorithm

    Get PDF
    We introduce a novel bio-inspired odor source localization algorithm (surge-cast) for environments with a main wind flow and compare it to two well- known algorithms. With all three algorithms, systematic experiments with real robots are carried out in a wind tunnel under laminar flow conditions. The algo- rithms are compared in terms of distance overhead when tracking the plume up to the source, but a variety of other experimentally measured results are provided as well. We conclude that the surge-cast algorithm yields significantly better performance than the casting algorithm, and slightly better performance than the surge-spiral algorithm

    A 3-D Bio-inspired Odor Source Localization and its Validation in Realistic Environmental Conditions

    Get PDF
    Finding the source of gaseous compounds released in the air with robots finds several applications in various critical situations, such as search and rescue. While the distribution of gas in the air is inherently a 3D phenomenon, most of the previous works have downgraded the problem into 2D search, using only ground robots. In this paper, we have designed a bio-inspired 3D algorithm involving cross-wind Levy Walk, spiralling and upwind surge. The algorithm has been validated using high-fidelity simulations, and evaluated in a wind tunnel which represents a realistic controlled environment, under different conditions in terms of wind speed, source release rates and odor threshold. Studying success rate and execution time, the results show that the proposed method outperforms its 2D counterpart and is robust to the various setup conditions, especially to the source release rate and the odor threshold

    Simulation Experiments with Bio-Inspired Algorithms for Odor Source Localization in Laminar Wind Flow

    Get PDF
    We compare three bio-inspired odor source localization algorithm (casting, surge-spiral and surge-cast) for environments with a main wind flow in simulation. The wind flow is laminar and the simulation setup similar to the setup in the wind tunnel in which we have carried out similar experiments with real robots. The algorithms are compared in terms of success rate and distance overhead when tracking the plume up to the source. We conclude that the algorithms based on upwind surge yield significantly better performance than pure casting

    Understanding the Potential Impact of Multiple Robots in Odor Source Localization

    Get PDF
    We investigate the performance of three bio-inspired odor source localization algorithms used in non-cooperating multi-robot systems. Our performance metric is the distance overhead of the first robot to reach the source, which is a good measure for the speed of an odor source localization algorithm. Using the performance distribution of single-robot experiments, we calculate an ideal performance for multi-robot teams. We carry out simulations in a realistic robotic simulator and provide quantitative evidence of the differences between ideal and realistic performances of a given algorithm. A closer analysis of the results show that these differences are mainly due to physical interference among robots

    Bio-Inspired, Odor-Based Navigation

    Get PDF
    The ability of moths to locate a member of the opposite sex, by tracking a wind-borne plume of odor molecules, is an amazing reality. Numerous scenarios exist where having this capability embedded into ground-based or aerial vehicles would be invaluable. The main crux of this thesis investigation is the development of a navigation algorithm which gives a UAV the ability to track a chemical plume to its source. Inspiration from the male moth\u27s, in particular Manduca sexta, ability to successfully track a female\u27s pheromone plume was used in the design of both 2-D and 3-D navigation algorithms. The algorithms were developed to guide autonomous vehicles to the source of a chemical plume. The algorithms were implemented using a variety of fuzzy controllers and ad hoc engineering approaches. The fuzzy controller was developed to estimate the location of a vehicle relative to the plume: coming into the plume, in the plume, exiting the plume, or out of the plume. The 2-D algorithm had a 60% to 90% success rate in reaching the source while certain versions of 3-D algorithm had success rates from 50% to 100%

    Design and Performance Evaluation of an Infotaxis-Based Three-Dimensional Algorithm for Odor Source Localization

    Get PDF
    In this paper we tackle the problem of finding the source of a gaseous leak with a robot in a three-dimensional (3-D) physical space. The proposed method extends the operational range of the probabilistic Infotaxis algorithm [1] into 3-D and makes multiple improvements in order to increase its performance in such settings. The method has been tested systematically through high-fidelity simulations and in a wind tunnel emulating realistic conditions. The impact of multiple algorithmic and environmental parameters has been studied in the experiments. The algorithm shows good performance in various environmental conditions, particularly in high wind speeds and different source release rates
    • 

    corecore