Chemical-Source Localization Using a Swarm of Decentralized Unmanned Aerial Vehicles for Urban/Suburban Environments

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9099

Jake A. Steiner, Joseph R. Bourne, Xiang He, D. Cropek, K. Leang

引用次数: 4

Abstract

In this paper, a decentralized chemical-source localization method is presented. In a real-world scenario, many challenges arise, including sporadic chemical measurements due to the complex interactions between the unmanned aerial vehicles (UAVs), the ambient air, and obstacles. The localization method is split into two phases: a search phase, where the agents cover the area and look for an initial chemical reading; followed by a convergence phase, where UAV agents utilize a particle swarm optimization (PSO) algorithm to locate the source of the chemical leak. The decentralized source-localization method enables a swarm of UAVs to safely travel in a complex environment and avoid obstacles and other agents while searching for the leaking source. The method is validated in simulation using realistic dynamic chemical plumes and through outdoor flight tests using a swarm of UAVs. The results demonstrate the feasibility of the approach.

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基于分散无人机群的城市/郊区环境化学源定位

本文提出了一种分散的化学源定位方法。在现实世界中，会出现许多挑战，包括由于无人机(uav)、环境空气和障碍物之间复杂的相互作用而导致的零星化学测量。定位方法分为两个阶段:搜索阶段，代理人覆盖该区域并寻找初始化学读数;随后是收敛阶段，其中无人机代理利用粒子群优化(PSO)算法来定位化学品泄漏的来源。分散源定位方法使无人机群能够在复杂环境中安全飞行，并在搜索泄漏源的同时避开障碍物和其他代理。该方法在真实动态化学羽流仿真和无人机群室外飞行试验中得到了验证。结果表明了该方法的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.40

自引率

66.70%

发文量

期刊介绍： This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.