Motion Planning Under Uncertainty: Application to an Unmanned Helicopter

Davis, Joshua D.; Chakravorty, Suman

doi:10.2514/1.25077

Cited by 10 publications

(2 citation statements)

References 20 publications

(15 reference statements)

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“…Trajectory tracking is an important area of research in the field of flight guidance, navigation and control and has been extensively studied for manned aircrafts (Kaminer, Pascoal, Hallberg, & Silvestre, 1998;Radmanesh, Kumar, & Sarim, 2018). A few interesting studies pertaining to unmanned vehicles include (Sujit, Saripalli, & Sousa, 2014;Davis & Chakravorty, 2007) that presents flight planning algorithms to control a UAV under different wind conditions. Yet, uncertainty in predicted trajectory caused to speed variations has mostly remained unexplored for UAVs.…”

Section: Trajectory Trackingmentioning

confidence: 99%

Uncertainty Quantification Framework for Autonomous System Tracking and Health Monitoring

Corbetta

Kulkarni

Banerjee

et al. 2021

IJPHM

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This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle.

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Section: Trajectory Trackingmentioning

confidence: 99%

Uncertainty Quantification Framework for Autonomous System Tracking and Health Monitoring

Corbetta

Kulkarni

Banerjee

et al. 2021

IJPHM

View full text Add to dashboard Cite

show abstract

“…For many aerial vehicle, this more realistic model is needed for stable control of the vehicle [22,32,35,53,59,104,109,111,136,140,145,159,178,[183][184][185]189]. Typically the states are constrained by limits on velocity and acceleration, and sometimes also on higher-order derivatives of position, and propulsion related to flight envelope.…”

Section: Point Vehicle With Differential Constraintsmentioning

confidence: 99%

Lighter than Air Robots

Sebbane

2012

Intelligent Systems, Control and Automation: Science and Engineering

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No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.Cover design: VTeX UAB, Lithuania

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Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

Kendoul

2012

Journal of Field Robotics

569

217

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Recently, there has been growing interest in developing unmanned aircraft systems (UAS) with advanced onboard autonomous capabilities. This paper describes the current state of the art in autonomous rotorcraft UAS (RUAS) and provides a detailed literature review of the last two decades of active research on RUAS. Three functional technology areas are identified as the core components of an autonomous RUAS. Guidance, navigation, and control (GNC) have received much attention from the research community, and have dominated the UAS literature from the nineties until now. This paper first presents the main research groups involved in the development of GNC systems for RUAS. Then it describes the development of a framework that provides standard definitions and metrics characterizing and measuring the autonomy level of a RUAS using GNC aspects. This framework is intended to facilitate the understanding and the organization of this survey paper, but it can also serve as a common reference for the UAS community. The main objective of this paper is to present a comprehensive survey of RUAS research that captures all seminal works and milestones in each GNC area, with a particular focus on practical methods and technologies that have been demonstrated in flight tests. These algorithms and systems have been classified into different categories and classes based on the autonomy level they provide and the algorithmic approach used. Finally, the paper discusses the RUAS literature in general and highlights challenges that need to be addressed in developing autonomous systems for unmanned rotorcraft. © 2012 Wiley Periodicals, Inc.

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Motion Planning Under Uncertainty: Application to an Unmanned Helicopter

Cited by 10 publications

References 20 publications

Uncertainty Quantification Framework for Autonomous System Tracking and Health Monitoring

Uncertainty Quantification Framework for Autonomous System Tracking and Health Monitoring

Lighter than Air Robots

Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

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