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Autonomous Pest Bird Deterring for Agricultural Crops Using Teams of Unmanned Aerial Vehicles

机译:使用无人驾驶飞行器的团队阻止农作物的自主害虫鸟

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An algorithm for autonomous bird deterring using teams of unmanned aerial vehicles (UAVs) is being proposed in this paper. Birds cause significant damage to commercial crops globally. A bird deterring system using autonomous UAVs can potentially overcome the limitations of current bird management methods. The problem of controlling UAVs to deter birds is formulated as a model predictive control problem. Occupancy gird map is used to construct a world model which represents the system's knowledge of the current location of target birds. A bird behaviour model and a sensor model are derived based on experimental results from a series of field trials. The future state of the target is estimated by propagating the centralised world model in time. Cooperative deterring missions are planned autonomously by optimising cost functions assigned to each UAV. A series of simulated scenarios are used to demonstrate the effectiveness of the system at driving UAVs to protect an 8 hectare area from multiple bird flocks. Furthermore, it is demonstrated that by cooperating a team of two UAVs, the same bird deterring mission can be completed more efficiently compared to using a single UAV.
机译:用无人驾驶飞行器(无人机)的自主鸟吓唬算法在本文中提出。鸟类对全球对商业作物造成重大损害。使用自主无人机的鸟类威慑系统可能会克服当前鸟类管理方法的局限性。控制无人机防止鸟类的问题被制定为模型预测控制问题。占用Gird地图用于构建世界模型,该世界模型代表了系统对目标鸟类当前位置的知识。鸟类行为模型和传感器模型是根据一系列现场试验的实验结果推导出来的。通过及时传播集中式世界模型,估计目标的未来状态。通过优化分配给每个UAV的成本函数来自主计划合作遏制任务。一系列模拟场景用于展示系统在驾驶无人机驾驶以保护8公顷的区域免受多个鸟类群的有效性。此外,据证明,通过合作两个无人机的团队,与使用单个UAV相比,可以更有效地完成相同的鸟类威慑任务。

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