ReviewFrontiers in robotics and AI2020
A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints.
Review in Frontiers in robotics and AI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Recent Developments and Applications of Drone Swarm: Techniques, Strategies, and Challenges.Sensors (Basel, Switzerland) · 2026Review
- Light driven polymer thin films as flying robotic chips in the sky.Lab on a chip · 2026Review
- Micro aluminum-air batteries for extended operational duration of small-scale quadrotors.Scientific reports · 2025Article
- An autonomous drone swarm for detecting and tracking anomalies among dense vegetation.Communications engineering · 2025Article
- Article
- A Survey of Robot Swarms' Relative Localization Method.Sensors (Basel, Switzerland) · 2022Review
- Experimental Verification on Steering Flight of Honeybee by Electrical Stimulation.Cyborg and bionic systems (Washington, D.C.) · 2022Article
- Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion.Frontiers in robotics and AI · 2022Article
- Data-driven CFD Scaling of Bioinspired Mars Flight Vehicles for Hover.Acta astronautica · 2021Article
- Sensors and Measurements for Unmanned Systems: An Overview.Sensors (Basel, Switzerland) · 2021Review
- Invariant Set Distributed Explicit Reference Governors for Provably Safe On-Board Control of Nano-Quadrotor Swarms.Frontiers in robotics and AI · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.