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Improved Area Covering in Dynamic Environments by Nonlinear Model Predictive Path Following Control

机译：控制下的非线性模型预测路径在动态环境中覆盖的改进区域

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The problem of area coverage using unmanned aerial vehicles is becoming increasingly important, e.g. for search-and-rescue missions, exploration, and environmental monitoring. The area coverage control problem spans multiple levels, from path planning to tracking and flight control, which are normally time scale separated. Area coverage in highly dynamic environments requires tight integration of these levels to achieve maximum performance and to avoid collision. We propose to fuse the path following level and the mobile platform control level to achieve better performance. By doing so, dynamic constraints, static and dynamic obstacle avoidance and energy efficiency are taken into account. Specifically, we shift the assignment of the reference path velocity from the planner to the controller which leads to improved performance while satisfying all system constraints and therefore increases safety and reliability. Simulation results for a quadcopter illustrate the performance of the proposed algorithm.
机译：使用无人驾驶飞行器的面积覆盖问题变得越来越重要，例如，用于搜索和救援任务，探索和环境监测。该区域覆盖控制问题跨越多个级别，从路径规划到跟踪和飞行控制，这通常是时间尺度分离。高动态环境中的区域覆盖需要紧密集成这些级别以实现最大性能并避免碰撞。我们建议融合级别后的路径和移动平台控制级别以实现更好的性能。通过这样做，考虑动态约束，静态和动态障碍物避免和能量效率。具体地，我们将参考路径速度的分配从策划器转换到控制器，该控制器导致性能提高，同时满足所有系统约束，因此增加了安全性和可靠性。 Quadcopter的仿真结果说明了所提出的算法的性能。

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