Multi-objective drone path planning for search and rescue with quality-of-service requirements
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Multi-objective drone path planning for search and rescue with quality-of-service requirements Samira Hayat1
· Ev¸sen Yanmaz2 · Christian Bettstetter1 · Timothy X. Brown3
Received: 9 July 2019 / Accepted: 12 June 2020 © The Author(s) 2020
Abstract We incorporate communication into the multi-UAV path planning problem for search and rescue missions to enable dynamic task allocation via information dissemination. Communication is not treated as a constraint but a mission goal. While achieving this goal, our aim is to avoid compromising the area coverage goal and the overall mission time. We define the mission tasks as: search, inform, and monitor at the best possible link quality. Building on our centralized simultaneous inform and connect (SIC) path planning strategy, we propose two adaptive strategies: (1) SIC with QoS (SICQ): optimizes search, inform, and monitor tasks simultaneously and (2) SIC following QoS (SIC+): first optimizes search and inform tasks together and then finds the optimum positions for monitoring. Both strategies utilize information as soon as it becomes available to determine UAV tasks. The strategies can be tuned to prioritize certain tasks in relation to others. We illustrate that more tasks can be performed in the given mission time by efficient incorporation of communication in the path design. We also observe that the quality of the resultant paths improves in terms of connectivity. Keywords Drones · UAVs · Path planning · Coverage · Connectivity · SAR · QoS
1 Introduction Unmanned aerial vehicles (UAVs), commonly called drones, are employed in search and rescue (SAR), monitoring and surveillance, network provisioning, and other applications. In many cases, a combination of aerial sensor coverage and wireless connectivity is desirable. For instance, in SAR and surveillance—where coverage enables target or event detection—connectivity ensures information dissemination to concerned authorities for quick response and situation awareness. Correspondingly, the drone flight paths should The work was supported by the ERDF, KWF, and BABEG (Grant KWF-20214/24272/36084 (SINUS)). Part of this work is linked to the Karl Popper Kolleg on Networked Autonomous Aerial Vehicles at the University of Klagenfurt. Samira Hayat performed a research stay at Carnegie Mellon University sponsored by a research leave grant from the University of Klagenfurt.
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Samira Hayat [email protected]
1
Institute of Networked and Embedded Systems, University of Klagenfurt, Klagenfurt, Austria
2
Lakeside Labs GmbH, Klagenfurt, Austria
3
Carnegie Mellon University, Kigali, Rwanda
allow the desired connectivity along with complete and successful coverage. Such path planning and optimization solutions are called connectivity-constrained coverage (Scherer and Rinner 2016) and connectivity-aware coverage (Flushing et al. 2013). Our work is motivated by the fact that different drone applications require different connectivity priorities (e.g., always, periodic, delay-tolerant). Path planning algorithms tunable to connec
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