移动机器人定位垃圾填埋场气体排放源:生物灵感是未来的发展方向吗?

Frontiers in neuroengineering Pub Date : 2012-01-12 eCollection Date: 2011-01-01 DOI:10.3389/fneng.2011.00020
Victor Hernandez Bennetts, Achim J Lilienthal, Patrick P Neumann, Marco Trincavelli
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引用次数: 105

摘要

机器人专家经常从动物身上获得灵感,设计传感器、执行器或控制机器人行为的算法。生物灵感来源于动物解决复杂任务的不可思议的能力,比如识别和操纵物体,在不平坦的地形上行走,或者导航到气味的来源。特别是追踪气味源头的任务,几乎完全是用生物学启发的算法来解决的,机器人已经被开发出来,例如,模仿飞蛾、屎壳郎或龙虾的行为。在本文中,我们认为用于气源定位的仿生方法是有限的,主要是因为动物的传感和驱动能力与最先进的气敏移动机器人有根本的不同。为了支持我们的说法,我们将移动机器人的驱动和化学传感与飞蛾的相应能力进行了比较。我们进一步表征了在四种原型环境中使用三种不同机器人平台(两个轮式机器人和一个飞行微型无人机)获得的气流和化学传感器测量结果,并表明恒定和单向气流的假设是许多气源定位方法的基础,通常是不有效的。这种分析应该有助于确定控制动物气源跟踪行为的基本原理如何有效地“转化”为充分考虑移动机器人能力的气源定位方法。我们还描述了一个参考应用的要求,用移动机器人监测垃圾填埋场的气体排放,并讨论了一种基于统计学的工程气源定位方法,作为生物启发算法的替代方案。
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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Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go?

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.

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