Nature-Inspired Drone Swarming for Real-Time Aerial Data-Collection Under Dynamic Operational Constraints

Hanno Hildmann (Corresponding Author), Ernö Kovacs, Fabrice Saffre, Abdel Isakovic

    Research output: Contribution to journalArticleScientificpeer-review

    2 Citations (Scopus)

    Abstract

    Unmanned Aerial Vehicles (UAVs) with acceptable performance are becoming commercially available at an affordable cost. Due to this, the use of drones for real-time data collection is becoming common practice by individual practitioners in the areas of e.g., precision agriculture and civil defense such as fire fighting. At the same time, as UAVs become a house-hold item, a plethora of issues—which can no longer be ignored and considered niche problems—are coming of age. These range from legal and ethical questions to technical matters such as how to implement and operate a communication infrastructure to maintain control over deployed devices. With these issues being addressed, approaches that focus on enabling collectives of devices to operate semi-autonomously are also increasing in relevance. In this article we present a nature-inspired algorithm that enables a UAV-swarm to operate as a collective which provides real-time data such as video footage. The collective is able to autonomously adapt to changing resolution requirements for specific locations within the area under surveillance. Our distributed approach significantly reduces the requirements on the communication infrastructure and mitigates the computational cost otherwise incurred. In addition, if the UAVs themselves were to be equipped with even rudimentary data-analysis capabilities, the swarm could react in real-time to the data it generates and self-regulate which locations within its operational area it focuses on. The approach was tested in a swarm of 25 UAVs; we present out preliminary performance evaluation.
    Original languageEnglish
    Article number71
    Number of pages25
    JournalDrones
    Volume3
    Issue number3
    DOIs
    Publication statusPublished - 4 Sep 2019
    MoE publication typeA1 Journal article-refereed

    Keywords

    • self-organization
    • adaptive behaviour
    • swarming algorithms
    • distributed sensing
    • multi-agent systems
    • nature-inspired optimization
    • artificial intelligence
    • swarm intelligence

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