Efficient Deployment of Energy-constrained Unmanned Aerial Vehicles in 3-dimensional Space

Efficient Deployment of Energy-constrained Unmanned Aerial Vehicles in 3-dimensional Space

6 Pages · 2016 · 2.62 MB · English

a number of recent study is putting effort into the development of UAV studies focuses on the mechanical implementation of the system. Only a few In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) - Volume 2, pages 446-451.

Efficient Deployment of Energy-constrained Unmanned Aerial Vehicles in 3-dimensional Space free download

446 Lee, H, Oh, J, Jeon, J and Lee, B Efcient Deployment of Energyconstrained Unmanned Aerial Vehicles in 3dimensional Space DOI: 105220/0005986904460451 In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) Volume 2 , pages 446451 ISBN: 9789897581984 Copyright c 2016 by SCITEPRESS – Science and Technology Publications, Lda All rights reservedEfcient Deployment of Energyconstrained Unmanned Aerial Vehicles in 3dimensional Space Hunsue Lee, Junghyun Oh, Jaedo Jeon and Beomhee Lee Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea Keywords: Multirobot Path Planning, Unmanned Aerial Vehicle, Deployment, Energy Constraint Abstract: In this paper, we present an efcient approach to deployment for unmanned aerial vehicles (UAVs) For a number of scattered tasks, we aim to minimize the duration of time that all UAVs reach their task locations In our previous work, we suggested the collaborative deployment algorithm for mobile robots using a carrier robot which transports and deploys the mobile robots However, the method worked only in 2dimensional plane where UAV could not be applied Therefore, this paper extends the previous work on 3dimensional space and gives the relevant algorithm Finally, we presents the feasibility of the proposed algorithm by simulation results 1 INTRODUCTION As unmanned aerial vehicle (UAV) is widely used, a number of recent study is putting effort into the development of UAV systems in robotics The ad vantages of using UAV platform are that the UAV is suitable for large scale operation such as exploration (Sujit and Beard, 2008)(Luotsinen et al, 2004)(Su jit et al, 2009), simultaneous localization and map ping (SLAM) (Caballero et al, 2009), mapbuilding (Yang et al, 2005), search and rescue (Doherty and Rudol, 2007)(Ryan and Hedrick, 2005), and surveil lance (Semsch et al, 2009) On the other hand, the use of multirobot system (MRS) is unavoidable because the system can pro vide exibility, faulttolerance, robustness, and cost effectiveness (Yan et al, 2013) To use multiple UAVs, the problem of multirobot task allocation has to be addressed However, the general task allocation problem is known to be nondeterministic polynomial (NP) hard, meaning that optimal solutions cannot be found quickly for large problems (Parker, 2008) The deployment problem is also related with the task al location problem Therefore, we need to reduce the amount of computation so that the efcient path can be generated within a nite time In this study, we use a team composed of two kinds of heterogeneous robots, one carrier robot (CR) and several UAVs, as shown in Figure 1 We as sume the CR has enough energy to complete a mis sion that is transporting and deploying the UAVs Figure 1: One Pioneer robot as the CR and two X12s as the UAVs The CR and the UAVs can be recognized and located by using the articial landmarks By using these two kinds of robots, the battery ex penditure of the UAV can be reduced and the total travel distance of the UAV can be increased There are a few studies that use this cooperative strategy (Wang et al, 2015)(Pei and Mutka, 2012)(Rybski et al, 2000)(Saska et al, 2012) However, most of the studies focuses on the mechanical implementation of the system Only a few existing studies discuss the path planning problem (Mei et al, 2006) Finding the optimal deployment path requires a lot of computa tion than the amount of computation for the traveling salesman problem (TSP) (Lee et al, 2015b) To re duce the computation, we divided the tasks into sev eral clusters based on the geographical information of the tasks Then each optimal deployment location for each cluster can be found Finally, the deployment locations are adjusted and merged into the solution Efcient Deployment of Energyconstrained Unmanned Aerial Vehicles in 3dimensional Space 447Although the solution does not guarantees the opti mality, the efcient path can be generated quickly The remainder of this paper is organized as fol lows In Section 2, we give brief description of the problem which has been presented in our previous work In the

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