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Optimal Control Problems in Drone Operations for Disaster Search and Rescue
This article concerns some optimal control problems arising in the operation of drone systems to support search and rescue operations in the aftermath of disasters, such as earthquakes, floods, tsunamis, extremal weather events, among others. More precisely, we focus on issues arising in multiple dr...
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Published in: | Procedia computer science 2021, Vol.186, p.78-86 |
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Main Author: | |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This article concerns some optimal control problems arising in the operation of drone systems to support search and rescue operations in the aftermath of disasters, such as earthquakes, floods, tsunamis, extremal weather events, among others. More precisely, we focus on issues arising in multiple drone systems to support communications and situational awareness for the planning and execution of agile and effective search and rescue operation in diverse disaster scenarios. This is of utmost importance to mitigate the impact of the destruction and, in particular, the loss of life.
After describing two key operation scenarios, we point out challenges arising in communications and control, and outline a systems design framework leading to the formulation of the several optimal motion control problems that, typically, feature state constraints. Then, a Pontryagin maximum principle in the Gamkrelidze form is presented. Under certain regularity assumptions, its measure multiplier is continuous, and, thus, amenable to the formulation of effective computational algorithms based on indirect methods.
Feedback control strategies to compensate for motion disturbances, on the one hand, and the optimization over multiple concatenated modes of operation - often entailing the need of different dynamic models -, on the other hand, are obtained by embedding the procedure to solve the optimal control problem in a receding horizon framework of the model predictive control type. |
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ISSN: | 1877-0509 1877-0509 |
DOI: | 10.1016/j.procs.2021.04.127 |