Talking to Autonomous Drones: Command and Control Based on Hierarchical Task Decomposition

Junger, Franz; Schopferer, Simon; Benders, Sebastian; Dauer, Johann C.

doi:10.1109/icuas51884.2021.9476753

Cited by 3 publications

(1 citation statement)

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“…For command and control planning, command and control is the hub of war, involving information transmission, information sharing, situation awareness, planning, decision execution, and many other aspects [10][11][12][13]. A new c2 interface (command and control interface, which is a critical component of various systems and networks) is proposed in a study that enables transmission using mission decomposition and hierarchical data formats, the operation of a UAS (an Unmanned Aerial System) at different levels of autonomy, and mission communication between ground control stations and airborne systems in a transparent and consistent manner in mission planning [14]. The authors' devised a control and planning method for the real-time monitoring of oceanic data.…”

Section: Introductionmentioning

confidence: 99%

Man-in-the-Loop Control and Mission Planning for Unmanned Underwater Vehicles

Han,

Wang,

Yuan

et al. 2024

JMSE

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UUVs (unmanned underwater vehicles) perform tasks in the marine environment under direction from a commander through a mother ship control system. In cases where communication is available, a UUV task re-planning system was designed to ensure task completion despite uncertain events faced by UUVs. First, the XML language standardizes the expression of UUV task elements. Second, considering the time sequence and spatial path planning requirements of human-supervised UUV control tasks, time sequence planning based on a genetic algorithm and spatial path planning based on an improved genetic algorithm were designed to plan near-optimal approximate spatial paths for control tasks. Third, uncertainties encountered during UUV task execution were classified so that the commander could adjust according to the situation or invoke the control task re-planning algorithm to re-plan. Finally, a simulation platform was built using the QT development environment to simulate human-supervised UUV control task planning and re-planning, verifying the algorithm’s design effectiveness.

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Section: Introductionmentioning

confidence: 99%