AeRove: A Compact Bimodal Aerial-Terrestrial Drone with Rapid Bistable Reconfiguration for Close-Range Pipeline Inspection

Caleb Polillio; Petras Swissler

cs.ROSep 17, 2026

AeRove: A Compact Bimodal Aerial-Terrestrial Drone with Rapid Bistable Reconfiguration for Close-Range Pipeline Inspection

Authors: Caleb Polillio, Petras Swissler

Organizations: Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ 07102

Abstract

Close-proximity pipeline inspection is challenging for standard drones due to high hovering power consumption, airflow sensitivity, and propeller wash interference with gas sensing. To address this, we present AeRove, a compact bimodal aerial-terrestrial robot. AeRove uses its propeller guards as wheels to roll along pipes and employs a spring-loaded bistable mechanism to reconfigure between ground and flight modes in 200 ms without continuous actuator power to maintain either state. The converging propeller-guard geometry also improves measured thrust efficiency. By reserving flight for obstacle hopping and using ground rolling for continuous traversal, current draw is reduced 14x compared to continuous flight (0.7 A vs. 10 A), extending estimated travel distance from 144 m to 2057 m. Autonomous trials on a 51 cm diameter steel pipe demonstrated navigation on straight and curved sections, obstacle jumping, and leak detection of simulated inspection markers. Separate CO2 sensing experiments evaluated gas-detection performance under perched and aerial operating conditions. Perched inspection produced a substantially larger concentration response than hovering under the tested conditions. During flight, an underbody propeller-intake configuration produced a faster and stronger gas-detection response than an extended probe by leveraging propeller-induced airflow. Code and designs are released under the CC-BY license.

Figures & tables

Fig. 1 : Composite front view of AeRove transforming from flight mode to rover mode as it lands on a ventilation duct.

Fig. 2 : AeRove shown without its battery in (Left) flight mode and (Right) terrestrial mode.

Fig. 3 : (Left) Photo, terrestrial drive wheel and motor in the flying configuration. (Right) CAD section view showing the (a) Tire; (b) Wheel; (c) Bearing; (d) Motor mount; (e) Arm to the main body; (f) Terrestrial drive DC motor; (g) Drive gear; (h) Flying brushless DC motor; (i) Propeller.

Fig. 4 : (Left) Physical transition mechanism, (Center) mechanical model in drive and flight mode (see Table I ), (Right) graph of spring energy vs displacement of the servo. Colors in left photos and right graph correspond.

Symbol	Parameter	Value
$a$	$AB$ length	16.00 mm
$b$	$BC$ length	12.53 mm
$l$	$CD$ length	10.00 mm
$r_{D}$	$OD$ length	9.417 mm
$r_{E}$	$OE$ length	15.832 mm
$\alpha$	Crossbar angle	$26.9^{\circ}$

TABLE I : Bistable Mechanism Parameters

Fig. 5 : Propeller-guard efficiency as a function of convergence angle.

Fig. 7 : (Left) AeRove transitioning from terrestrial to flight mode and taking off. (Right) AeRove transitioning from flight to terrestrial mode before driving. Note that the actual transition between modes is

200\text{\,}\mathrm{ms}

Parameter	Value
Dry Weight	130 g
Battery Mass	45 g
Total Mass	175 g
Transition Time	200 ms
Transition Angle	75 ∘
Thrust-to-Weight Ratio	2.7:1

TABLE II : AeRove Physical Specifications

Mode	Speed	Current	Time	Range
Inspection (Drive)	40 cm/s	0.7 A	85.7 min	2057 m
Inspection (Flight)	40 cm/s	10 A	6.0 min	144 m
Max Speed (Drive)	40 cm/s	0.7 A	85.7 min	2057 m
Max Speed (Flight)	8 m/s	20 A	3.0 min	1440 m

TABLE III : Estimated Operating Performance

Platform	Weight	Flight Endurance	Drive Endurance	Flight Speed	Ground Speed
AeRove	175 g	6 min	84 min	9 m/s	0.4 m/s
AeRove (flight hardware only)	130 g	12.5 min	N/A	>9 m/s	N/A
Micro Aerial-Ground Robot [ 23 ]	41 g	5 min	42 min	0.3 m/s	0.1 m/s
Drivocopter [ 24 ]	4500 g	8 min	90 min	–	1.0 m/s
HyTAQ [ 25 ]	450 g	7 min	27 min	2.0 m/s	1.5 m/s
Comparison quadrotor [ 26 ]	69 g	9 min	N/A	21 m/s	N/A

TABLE IV : Comparison of AeRove with aerial-terrestrial, pure flight, and inspection platforms.

Fig. 8 : Representative data for perched and hovering measurements. Graph shows CO 2 detection in parts per million.

Fig. 9 : Representative test data for propeller intake and probe measurements. Graph shows CO 2 detection.

Fig. 10 : Autonomous pipeline inspection trials: (top) terrestrial traversal through a 90° bend; (middle) transition from horizontal ground inspection to aerial inspection of a vertical pipe section; (bottom) obstacle detection, aerial traversal, landing, and resumed terrestrial locomotion.

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A Modular Dual-Camera Pipeline for Micro-Inspection Using Aerial Robots

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Aerial Robotics Robotic Inspection Vision-Based Robot Control

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Most existing drone-based inspection systems require the drone to fly dangerously close to the target or follow complex flight paths to capture small details. In addition, drone flight is affected by disturbances and localization inaccuracies, which can cause the drone to lose sight of its supposed target when it has a narrow view. Furthermore, trajectory planning often requires prior information about the target's geometry, position, and orientation, which is not always available for non-structural targets such as trees, vehicles, or people. To address these challenges, this paper presents aerial_micro_inspection, a generic pipeline for aerial micro-inspection across different use cases. The pipeline assumes a PX4-powered drone equipped with two cameras: (i) a zoomed, gimbal-mounted inspection camera that captures fine details without requiring the drone to fly very close to the target, and (ii) a wide-field-of-view stereo navigation camera that acquires the target surface on site, estimates its range, and partitions it into smaller inspection regions. In addition, a vision-based feedback loop compensates for drone motion while the inspection camera visits small partitions of a larger surface. We evaluate the pipeline in simulation and real-world experiments, mainly in two use-case scenarios: tree inspection for detecting oak processionary caterpillars and their eggs, and greenhouse inspection of sticky traps for detecting whiteflies. The results show improved coverage robustness under drone disturbances in simulation, as well as effective detection of caterpillars and eggs and high-detail imaging of insects in real-world experiments. The pipeline is open-source, developed in ROS 2, and can be adapted to new applications by replacing the surface-segmentation and micro-target detection checkpoints. The code is available at: https://github.com/SaxionMechatronics/aerial_micro_inspection

Abstract

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