Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment

Abstract

Swarms of tiny flying robots hold great potential for exploring unknown, indoor environments. Their small size allows them to move in narrow spaces, and their light weight makes them safe for operating around humans. Until now, this task has been out of reach due to the lack of adequate navigation strategies. The absence of external infrastructure implies that any positioning attempts must be performed by the robots themselves. State-of-the-art solutions, such as simultaneous localization and mapping, are still too resource demanding. This article presents the swarm gradient bug algorithm (SGBA), a minimal navigation solution that allows a swarm of tiny flying robots to autonomously explore an unknown environment and subsequently come back to the departure point. SGBA maximizes coverage by having robots travel in different directions away from the departure point. The robots navigate the environment and deal with static obstacles on the fly by means of visual odometry and wall-following behaviors. Moreover, they communicate with each other to avoid collisions and maximize search efficiency. To come back to the departure point, the robots perform a gradient search toward a home beacon. We studied the collective aspects of SGBA, demonstrating that it allows a group of 33-g commercial off-the-shelf quadrotors to successfully explore a real-world environment. The application potential is illustrated by a proof-of-concept search-and-rescue mission in which the robots captured images to find “victims” in an office environment. The developed algorithms generalize to other robot types and lay the basis for tackling other similarly complex missions with robot swarms in the future.

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

“…When the control is decentralized, but the MAVs benefit from an external positioning system, or vice versa, the largest swarms are in the dozens (Hauert et al, 2011;Vásárhelyi et al, 2018;Weinstein et al, 2018). For swarms featuring both local perception and distributed control, the highest numbers are currently in the single digits (Nägeli et al, 2014;Guo et al, 2017;Saska et al, 2017;McGuire et al, 2019). Despite the fact that these numbers have been increasing in the last few years, they are still lower, as the operations are shifted away from external system and toward on-board perception and control.…”

“…Distributed sensing behaviors may require the swarm to travel in a formation or flock, but may also include behaviors in which the swarm distributes over pre-specified areas (Bähnemann et al, 2017) or disperses (McGuire et al, 2019). Collaborative transport and object manipulations take two forms.…”

80 Years of Aerospace Engineering Education in the Netherlands

“…The DelFly is still one of its greatest successes. The DelFly has increasingly been miniaturized from a 50 centimeters wingspan and weighing 21 grams (2005) to only 3.07 grams and a wingspan of 10 cm [33] [34]. The newest addition to the project is DelFly Nimble, which is tailless, weighs 29 grams and has a wingspan of 33 centimeters and actually mimics the movement of an insect when flying.…”

SwarmLab: a Matlab Drone Swarm Simulator

“…Drone applications have exploded in the last decade and, recently, the availability of inexpensive hardware has awoken the interest for aerial swarms where several flying robots collaborate to achieve a collective task [1], [2]. Benefits of multi-drone systems are envisioned for a wide range of missions including search and rescue [3], long-term monitoring [4], sensor data collection [5], indoor navigation [6], environment exploration [7], and cooperative grasping and transportation [8]. In the entertainment industry, the latest challenge is the coordination of fleets of hundreds of drones that light up the night sky with aerial shows, as Intel 1 and Ehang 2 have displayed.…”