Controlling swarm complexity: a management by objective approach

Segor, Florian; Tchouchenkov, Igor; Buller, Aleksej; Kollmann, Matthias; Müller, Wilmuth

doi:10.1117/12.2518608

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…In addition, the algorithms will be tested in other real applications and combined with specialized AI providing automatically behavior adaptation in different situationsespecially by goal-oriented control focusing on results provided by the executing assets [8], [10].…”

Section: Discussionmentioning

confidence: 99%

“…The base algorithms used for deterministic area coverage path planning were been described in [3], [7] and compared with other approaches. In contrast to the solutions, the main advantage of the deterministic approach used here is the holistic view of a reconnaissance mission, which is not limited to the area to be explored and the exploration [8]. Rather, the entry and exit strategies are also planned and optimizing the ramp-up and return trip times considering known exclusion criteria (e.g.…”

Section: State Of the Artmentioning

confidence: 99%

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Area reconnaissance with heterogeneous groups of autonomous systems

Tchouchenkov,

Segor,

Buller

et al. 2023

Emerging Imaging and Sensing Technologies for Security and Defence VIII

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In the last years Unmanned Vehicles for different environments (UxVs) have been recognized as relevant game changers and key technologies for a wide range of military and civilian applications. Even parallel deployment of heterogeneous autonomous assets as swarms or cooperating teams is no longer science fiction, but a realistic operational scenario. Their effectiveness can be significantly increased by optimization of number, capabilities and application strategy of assigned assets, particularly in time-critical tasks (e.g. the search for missing persons, disaster relief and modern warfare operations). Especially, the movements of the vehicles must be carefully managed. However, the use of small UxVs is often accompanied by limitations like short mission time and limited sensor coverage, which in turn can be compensated by the intelligent assignment of vehicles with increased autonomy in cooperating groups.An important basis for efficient and effective autonomous reconnaissance, especially in swarming or teaming scenarios, is movement optimization considering the capabilities of the heterogeneous vehicles equipped with different sensor systems operating in a combined mission. For this purpose, algorithms have been developed at Fraunhofer IOSB that enable appropriate planning and dynamic processing in very different situations for heterogeneous groups of cooperating vehicles. In the following, two exploration methods for reconnaissance missions are described in more detail and their possibilities are discussed and compared.

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Section: Discussionmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%