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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.
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This content will become publicly available on June 1, 2027
Moth: A Low-Cost IR-Based Approach towards Autonomous Precision Drone Landing
Here's the cleaned-up version: As micro-drones become increasingly deployed in indoor environments for applications ranging from warehouse inspection to emergency response, the challenge of precise automated landing emerges as a crucial barrier to their practical operation and ubiquitous adoption. Existing landing approaches often require complex hardware and substantial computation or perform unreliably indoors, making them impractical for palm-sized microdrones. We propose Moth, a low-cost infrared light-based solution that targets precise and efficient landing of low-resource microdrones. Moth consists of an infrared light source at the landing station along with an energy-efficient photodiode (PD) sensing platform attached to the bottom of the drone. At a cost under 83 USD, Moth achieves comparable performance to vision-based methods but at a fraction of the energy consumption and computation. Moth requires only three PDs without any complex pattern recognition models to land the drone accurately, under 10 cm of error, from up to 11.1 m away.
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- Award ID(s):
- 1943396
- PAR ID:
- 10690106
- Publisher / Repository:
- IEEE
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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