Plume Source Localization Research Articles

A Quantum-Based Collaborative Guidance Strategy for Multirobot Plume Source Localization

A Quantum-Based Collaborative Guidance Strategy for Multirobot Plume Source Localization

A planning method for chemical plume tracking and source localization with autonomous underwater vehicle

Underwater target detection technology is an important mean for the development and utilization of marine environment, which has important value in commercial and military fields, and is valued by more and more experts, scholars, and production technicians. A set of behavior-based autonomous underwater vehicle online task planning algorithm is proposed in this article, including the searching behavior based on changing Z-shape, tracking behavior based on an improved artificial potential field theory with likelihood map, rediscovering behavior, and source declare behavior. The proposed algorithm can improve the accuracy and success rate of autonomous underwater vehicle to the source positionficient. The physical experiment results verify the effectiveness of the proposed method.

Multi-Robot Plume Source Localization by Distributed Quantum-Inspired Guidance With Formation Behavior

Multi-Robot Plume Source Localization by Distributed Quantum-Inspired Guidance With Formation Behavior

Self-coloring-Driven Plume Source Localization Strategy for Multiple Robots Using Dirichlet Process Gaussian Mixture Model and Mutation Random Salp Swarm Algorithm

Self-coloring-Driven Plume Source Localization Strategy for Multiple Robots Using Dirichlet Process Gaussian Mixture Model and Mutation Random Salp Swarm Algorithm

Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network

Marine pollution, which can cause damage to marine ecosystems, cut fishery production, and even harm human health, has aroused worldwide interest in recent years. Marine pollution reduction operations can stagnate in the case that the source of the pollution is unknown or hidden. In this paper, we present a novel method for marine pollution source localization using a network of mobile sensor nodes, such as autonomous underwater vehicles equipped with chemical sensors. Traditional reactive control methods can respond quickly to the shape dynamics of a chemical plume; however, they can hardly achieve intelligent cooperation unlike deliberative methods. In this study, we present a behavior composition method that attempts to combine the advantages of reactive and deliberative methods. An upwind-customized crossover operation based on the genetic algorithm was formulated as one of the elementary behaviors. The upwind sprint and movement away from the centroid of the sensor nodes were also modeled as another two elementary behaviors. Different sensor nodes are capable of different simultaneous elementary behaviors, enabling behavior composition in the mobile sensor network during plume source localization. The proposed method was evaluated using a widely used filamentous plume simulation platform, which has been used to facilitate field experiments in real marine environments. Simulation results indicate that the proposed method achieved high time-efficiency and localization accuracy during plume source localization in marine environments.

Outdoor acoustic measurements of a rocket engine hot fire test

The primary focus of this paper is to present the results from outdoor acoustic measurements from a rocket engine hot fire test. The two objectives of the test were to quantify the overall sound pressure level at large distances from engine to quantify the impact of the events on the surrounding community, and to better understand the source distribution in the engine plume. Microphone arrays were positioned around the property to be able to capture the sound propagation, estimate source levels, and conduct phased array-based plume source localization.

Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment

Reliable tracking of the plume by the robot is the key to achieving plume source localization. To address the problem of low success rate and long search time of robot source location due to the unavailability of reliable information on gas diffusion flow direction and flow velocity in an indoor weak wind environment, a hybrid strategy is proposed to improve the grey wolf optimization algorithm for robot plume tracking and location. The plume is modeled using Computational Fluid Dynamics (CFD) in a two-dimensional indoor weak wind environment, and the plume concentration value is used as the individual adaptation degree of the algorithm. Without carrying plume velocity and flow direction sensors, the source-finding robot simulates the grey wolf population’s social mechanism and hunting behavior to update its position. The improved Grey Wolf Optimization algorithm is compared with the traditional Grey Wolf Optimization (GWO), Particle Swarm Algorithm (PSO), and Genetic Algorithm (GA) in simulation experiments. The simulation experiments show that the average number of iterations of the improved GWO is 7, 108, and 118 times shorter than the four source finding algorithms of GWO, PSO, and GA. The average planning path is reduced by 0.91 meters, 2.35meters, and 2.90 meters. 3s, 10.3s, and 9.3s reduce the average running time. The average positioning success rate is improved by 30%, 32%, and 40%. The applicability and Stability of the improved GWO algorithm in solving the plume tracking and localization problem in an indoor weak wind environment are verified.

Plume Source Localization on Enceladus by Sequential Monte Carlo Method

Water vapor plumes emanating from the geyser vents at Enceladus’s south pole area invite the possibility of direct access to the subsurface liquid reservoir to acquire pristine biological material if it exists. Any descending lander adapted for plume localization is required to not only explore the icy plume environment during its descent, but it must also infer the location of the landing target—the plume source—autonomously. Compared with existing scenarios of terrestrial plume source localization methods, the source likelihood map (SLIM) method for an Enceladus mission offers a more extensive search area, a higher maneuver velocity, and a shorter search time. This paper investigates a particle-based odor source localization (pOSL) approach that offers the prospect of targeting one of the plume sources by autonomously measuring the concentration field. Reasons for the negative likelihood and overfitting issues associated with Bayesian SLIM are analyzed to build a novel probabilistic model. By implementing this model, the proposed pOSL algorithm evaluates the observation likelihood via posterior maximization method and estimates the source location via the sequential Monte Carlo method. The pOSL algorithm resolves difficulties associated with other methods while reducing the time complexity from to . The numerical simulations illustrate that the proposed approach is feasible and permits accurate targeting of Enceladus’s geyser vents.

3D framework combining CFD and MATLAB techniques for plume source localization research

Sudden release of chemical gas in indoor environments such as office like buildings or industrial warehouses could become hazardous. This has highlighted the need to localize the source of contaminant in least time possible using plume-tracing mobile robots. A simulation framework is outlined for developing, optimizing and testing plume-tracing algorithm in a complex environment. In the first part of the framework, fluid flow process and contaminant transport in a virtual environment is generated by dedicated computational fluid dynamics (CFD) software packages. In the second part of the framework, the exported data of airflow vectors and contaminant concentration from the CFD results is used for the robot to find the source of the contaminant in a three-dimensional search pattern. The virtual robot is built using an in house developed MATLAB code. Several case studies have been addressed at the same complex environment, however at different source locations and source heights as well as at different initial robot start positions. Based on the results, the paper shows the capability of the 3D plume-tracing framework to find the location of the source for strong 3D indoor plumes when tested at different cases.

Miniature mobile robots for plume tracking and source localization research

Cooperating robots have the potential to achieve tasks more quickly and with a higher probability of success than a single robot acting alone. This paper describes a system of miniature mobile robots and the algorithms used to demonstrate cooperative plume tracking and source localization. The robots share sensor information, but individually decide a course of action based on their estimate of the plume field. This algorithm was implemented on a group of miniature mobile robots capable of measuring temperature plumes. Experimental results are presented for a representative test run. We also describe a 'next-generation' miniature mobile robot under development that occupies only 0.25 cubic inches in volume.