A Comparison of Automatic Nap‐of‐the‐earth Guidance Strategies for Helicopters

Johnson, Eric N.; Mooney, John G.

doi:10.1002/rob.21514

Cited by 20 publications

(11 citation statements)

References 17 publications

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“…While some systems are focused on above ground flight planning (E.N. Johnson & Mooney, 2014;Pettersson & Doherty, 2004;Scherer, Singh, Chamberlain, & Elgersma, 2008;Vachtsevanos, Tang, Drozeski, & Gutierrez, 2005;Whalley et al, 2016;Wzorek et al, 2006), others are solely focused on the approach to land phase of flight (Taamallah, Bombois, & VandenHof, 2017;Yomchinda, Horn, & Langelaan, 2011;Zimmermann, 2016). Our approach is similar to Fabiani, Fuertes, Piquereau, Mampey, and Teichteil-Konigsbuch (2007), Lantzch et al (2012), and Scherer et al (2012) in that it plans through all phases of flight (from takeoff to land) for a full sized rotor-craft.…”

Section: Unmanned Rotorcraftsmentioning

confidence: 99%

“…Some approaches rely on a single planner to navigate the aerial vehicle through its entire route (E.N. Johnson & Mooney, 2014;Hrabar, 2008;Scherer et al, 2012). To address computational intractability encountered using a single planner/configuration over larger distances, the planning problem is usually divided between a coarse (waypoint path) global planner and a finer (smooth trajectory) local planner.…”

Section: Unmanned Rotorcraftsmentioning

confidence: 99%

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High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Choudhury

Dugar

Maeta

et al. 2019

Journal of Field Robotics

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Autonomous flight of unmanned full‐size rotor‐craft has the potential to enable many new applications. However, the dynamics of these aircraft, prevailing wind conditions, the need to operate over a variety of speeds and stringent safety requirements make it difficult to generate safe plans for these systems. Prior work has shown results for only parts of the problem. Here we present the first comprehensive approach to planning safe trajectories for autonomous helicopters from takeoff to landing. Our approach is based on two key insights. First, we compose an approximate solution by cascading various modules that can efficiently solve different relaxations of the planning problem. Our framework invokes a long‐term route optimizer, which feeds a receding‐horizon planner which in turn feeds a high‐fidelity safety executive. Secondly, to deal with the diverse planning scenarios that may arise, we hedge our bets with an ensemble of planners. We use a data‐driven approach that maps a planning context to a diverse list of planning algorithms that maximize the likelihood of success. Our approach was extensively evaluated in simulation and in real‐world flight tests on three different helicopter systems for duration of more than 109 autonomous hours and 590 pilot‐in‐the‐loop hours. We provide an in‐depth analysis and discuss the various tradeoffs of decoupling the problem, using approximations and leveraging statistical techniques. We summarize the insights with the hope that it generalizes to other platforms and applications.

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Section: Unmanned Rotorcraftsmentioning

confidence: 99%

Section: Unmanned Rotorcraftsmentioning

confidence: 99%

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Choudhury

Dugar

Maeta

et al. 2019

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…However, solely optical flow‐based solutions cannot cope well with frontal obstacles, and these methods are not well suited for omnidirectional obstacle avoidance as needed for our scenario. Johnson and Mooney () use reactive obstacle avoidance on a small helicopter for velocities up to 12 m/s. Heng et al.…”

Section: Related Workmentioning

confidence: 99%

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

Droeschel

Nieuwenhuisen

Beul

et al. 2015

Journal of Field Robotics

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Micro aerial vehicles, such as multirotors, are particularly well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance or disaster management. Key prerequisites for the fully autonomous operation of micro aerial vehicles are real-time obstacle detection and planning of collision-free trajectories. In this article, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a three-dimensional (3D) laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. Local maps are efficiently merged in order to simultaneously build global maps of the environment and localize in these. For autonomous navigation, we generate trajectories in a multilayered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach and the involved components in simulation and with the real autonomous micro aerial vehicle. Finally, we present the results of a complete mission for autonomously mapping a building and its surroundings. C 2015 Wiley Periodicals, Inc.

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“…They are one of the first modern applications in robotics research. From the early days, AGV has been constantly being improvised to provide advanced driver assistance, road safety, and collision avoidance [ 6 , 7 , 8 ]. However, research in the unstructured environment still falls behind compared to structured environments.…”

Section: Introductionmentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

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When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

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A Comparison of Automatic Nap‐of‐the‐earth Guidance Strategies for Helicopters

Cited by 20 publications

References 17 publications

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

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