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Exploiting Land Transport to Improve the UAV's Performances for Longer Mission Coverage in Smart Cities

机译:开发陆路运输以提高无人机的性能,以延长智慧城市的任务覆盖率

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This contribution presents a solution to improve the performances of micro unmanned aerial vehicles (UAVs) by increasing their missions coverage in terms of distance and time. This is achieved by letting the UAV ride existing land public transport such as the city bus throughout the route to its mission location. Indeed, due to their limited battery capacity, micro-UAVs flying time is restrained, which affects their mission and coverage performances. In this paper, we propose to leverage the use of public transport infrastructure, such as city buses, to carry the UAVs whenever it is possible in order to minimize their flight energy consumption. For this purpose, a generic scheduling framework to efficiently cover spatially and temporally distributed events in a geographical area of interest over a long period of time is proposed. By considering the public transport schedule table, a mixed integer linear programming problem (MILP) aiming at minimizing the total energy consumption of the UAVs is formulated while accomplishing all the pre-scheduled missions. The proposed proactive UAV scheduling framework optimizes the UAV trips according to the mission occurrence and the schedule table of the buses. The obtained results demonstrate the effectiveness of the collaboration between the UAVs and the land transport to improve the overall UAV missions' performances in terms of distance coverage.
机译:这一贡献提出了一种解决方案,可通过增加其在距离和时间方面的任务覆盖率来改善微型无人机的性能。这是通过让无人机在到达任务地点的整个路线上乘坐现有的陆地公共交通工具(例如城市公交车)来实现的。确实,由于其有限的电池容量,微型无人机的飞行时间受到限制,这影响了它们的任务和覆盖性能。在本文中,我们建议利用公共交通基础设施(例如城市公交车)尽可能携带无人机,以最大程度地降低其飞行能耗。为此,提出了一种通用的调度框架,可以有效地覆盖很长一段时间内感兴趣的地理区域中时空分布的事件。通过考虑公共交通时间表,制定了混合整数线性规划问题(MILP),旨在最小化无人机的总能耗,同时完成所有预定任务。所提出的主动式无人机调度框架根据任务的发生和公共汽车的调度表来优化无人机的行程。获得的结果证明了无人机与陆路运输之间进行合作以提高无人机在距离覆盖范围内的总体性能的有效性。

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