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ABSTR ACT
The aim of this paper is to explore bioinspired
vertiport designs—a hub for drones’ vertical takeoff
and landing (VTOL) and servicing, also referred to
as a nesting station, docking station, hangar, or
landing station—for drone swarms tasked with specific
missions. The vertiport system design is inspired
by tree structures, with branches represented by
capsules that house drones. Solar panels mounted
on actuators at the top of the vertiport adjust their
orientation to maximize sun exposure, supplying
power to the vertiport’s isolated grid for continuous
energy day and night. A weather station located
at the top transmits data to a computing system,
ensuring environmental safety for drone operations.
The vertiport’s key components include capsules that
open and close during drone launch and landing.
Each capsule is equipped with charging contacts for
the drones, AprilTags to facilitate precise landing, and
a mechanism to center the drone within the capsule
upon closure. Designed to protect the drones from
environmental conditions, these capsules feature
robust structures capable of withstanding harsh
weather, ensuring the drones are safeguarded inside.
This design highlights the potential of bioinspired
approaches in creating efficient vertiport systems.
KEYWORDS: AprilTags, bioinspiration, drones, vertiports,
precision landing
1. Introduction
Orphaned, abandoned, or inactive wells are natural oil and gas
wells that have been decommissioned or left unmanaged by
fossil fuel extraction companies due to low production capacity
or lack of economic viability. Once a well is declared aban-
doned, the extraction company deactivates it. While these wells
no longer produce oil or gas, they continue to emit methane
into the environment. Methane, a flammable natural gas, is
harmful to both the environment and human health and is a
significant contributor to global warming due to its impact on
the ozone layer [1].
Methane emissions from orphaned wells occur because
many of these wells are left unplugged after being aban-
doned plugging wells is an expensive process. It is estimated
that across the United States, there are approximately 2.15
million unplugged orphaned wells still releasing methane
and carbon dioxide, with an annual emission rate equiva-
lent to 7.11 million metric tons. This amount of greenhouse
gas is comparable to the emissions produced by 1.54 million
vehicles running for a year [2]. To address this issue, the US
Department of the Interior allocated US$4.7 billion in January
2022 to plug orphaned wells across the country [3]. However,
climate-focused think tanks estimate that fully plugging all
documented orphaned wells in the US could cost around
US$280 billion [4]. Given the high cost of the plugging process,
it would be more efficient to prioritize wells that are emitting
methane at the highest rates. To do this effectively, we must
first identify which wells are the largest emitters at each site.
This task is challenging because many orphaned wells are
located in remote areas that are difficult for humans to access
and can be hazardous to their health. Therefore, developing an
automated process that can autonomously survey these sites
and detect wells with high methane emission rates without
human intervention would be highly effective. In this paper,
we propose a method to identify orphaned wells with high
methane emissions using a drone vertiport concept. Inspired
by nature, this concept combines an autonomous vertiport
system with a drone swarm [5].
The drone vertiport system supports the detection of
methane emissions from orphaned wells by serving as a
storage, charging, and landing hub for drones equipped with
sensing tools. It also offers a valuable solution for enabling
maintenance tasks in smart cities, towns, and villages [6–11].
While the emphasis of this work is on the design and function-
ality of the bioinspired vertiport system, the methane detection
BIOINSPIRED VERTIPORT SYSTEM DESIGN FOR
SUPPORTING DRONE SWARMS IN METHANE
GAS DETECTION FROM ORPHANED WELLS
FAHAD MANNAN†, LOGAN MOORE†, JORGE QUIROGA‡, ARTHUR WIETHARN†,
SIHUA SHAO§, XIANG SUN††, AND MOSTAFA HASSANALIAN*†
ME
|
TECHPAPER
*Corresponding author: mostafa.hassanalian@nmt.edu
† Department of Mechanical Engineering, New Mexico Tech, Socorro, NM 87801
‡ Department of Electrical Engineering, New Mexico Tech, Socorro, NM 87801
§ Department of Electrical Engineering, Colorado School of Mines, Golden, CO
80401
†† Department of Electrical and Computer Engineering, University of New
Mexico, Albuquerque, NM 87131
Materials Evaluation 83 (4): 36–50
https://doi.org/10.32548/2025.me-04484
©2025 American Society for Nondestructive Testing
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