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Bio-inspired UAV swarm flocking guided by information-theoretic principles

Project description

Information-theoretic principles for UAV swarms with synchronised behaviour

Creating large UAV swarms with synchronised behaviour is challenging. The influential neighbourhoods theory provides a strategy for maintaining group cohesion, reducing cognitive load, and guiding self-organised collective motion. Supported by the Marie Skłodowska-Curie Actions (MSCA) programme, the BUGI project aims to develop a framework for designing UAV swarms using information-theoretic principles. These principles include empowerment, relevant information, and the bio-inspired influential neighbourhood principle. The project focuses on creating control mechanisms for guiding collective motion and establishing measures for characterising swarm cohesion in real-time. The goal is to enable the development of adaptive and scalable UAV swarms that can maintain cohesion in uncertain and complex dynamic environments.

Objective

Autonomous UAV swarms emerge as a disruptive technology with impact on many areas of our life, from monitoring key ecosystems and hazardous environments to enabling advanced data insights. However, creating large, decentralized UAV swarms with synchronized flocking behavior and autonomous collision avoidance is a challenge. This project aims to develop a holistic framework for supporting the design of UAV swarms based on generic information-theoretic principles - empowerment and relevant information - complemented with the bio-inspired influential neighbourhood principle. The key objectives are to design locally optimized control mechanisms guiding the collective motion and to provide theoretical measures for real-time characterization of swarm cohesion. The influential neighborhoods theory provides an efficient strategy for maintaining group cohesion in the coordinated motion of animal groups. It allows agents to limit their attention to a small subset of their neighbors, reducing information processing and cognitive load and offers explicit and concise models that can be implemented directly. Empowerment models agents behaviour with the principle of keeping ones options open, assuming that, maximizing the future potential outcome of ones actions is a survivability driver in nature. Relevant information provides a measure, quantifying the minimal amount of information an agent needs to process in order to achieve a certain level of utility. Identifying influential neighbourhoods dynamically will serve as a base for maximizing agents empowerment which will guide the UAV swarm self-organized collective motion. The integration of relevant information, influential neighbourhood identification and empowerment maximization into a novel principled framework for optimizing agents decision-making will provide enablers for the future adaptive and scalable UAV swarms, maintaining swarm cohesion while manoeuvring in uncertain and complex dynamic environments.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 211 754,88
Total cost

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No data

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