Hybrid A* path search with resource constraints and dynamic obstacles
Hybrid A* path search with resource constraints and dynamic obstacles
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DOI：
10.3389/fpace.2022.1076271
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Alan C. Cortez;Bryce T. Ford;I. Nayak;S. Narayanan;Mrinal Kumar
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作者：
Alan C. Cortez;Bryce T. Ford;I. Nayak;S. Narayanan;Mrinal Kumar
本文考虑了无人机 (UAV) 的资源约束和动态障碍的路径规划，建模为杜宾斯代理。在制导阶段纳入这些复杂的约束，扩大了无人机在包含路径相关积分约束和时变障碍的挑战性环境中的操作范围。当无人机处于危险环境中，使其在其经过的路径上遭受累积损坏时，就会发生路径相关的积分约束，也称为资源约束。选择噪声惩罚函数作为本研究的资源约束，该函数被建模为对无人机施加路径相关负载的路径积分，规定不超过上限。风暴、湍流和冰等天气现象被建模为动态障碍物。在本文中，利用航空气象服务的冰数据来创建训练数据集，以学习冰现象的动态。动态模式分解（DMD）用于学习和预测飞行层面冰况的演变。这种方法被认为是一种传播障碍物动力学的计算可扩展的方法。时变冰障碍物的降阶 DMD 表示与最近开发的回溯混合 A* 图搜索算法集成。回溯机制允许我们在存在资源限制的情况下以计算可扩展的方式确定可行路径。给出了说明性的数值结果来证明所提出的路径规划方法的有效性。
This paper considers path planning with resource constraints and dynamic obstacles for an unmanned aerial vehicle (UAV), modeled as a Dubins agent. Incorporating these complex constraints at the guidance stage expands the scope of operations of UAVs in challenging environments containing path-dependent integral constraints and time-varying obstacles. Path-dependent integral constraints, also known as resource constraints, can occur when the UAV is subject to a hazardous environment that exposes it to cumulative damage over its traversed path. The noise penalty function was selected as the resource constraint for this study, which was modeled as a path integral that exerts a path-dependent load on the UAV, stipulated to not exceed an upper bound. Weather phenomena such as storms, turbulence and ice are modeled as dynamic obstacles. In this paper, ice data from the Aviation Weather Service is employed to create training data sets for learning the dynamics of ice phenomena. Dynamic mode decomposition (DMD) is used to learn and forecast the evolution of ice conditions at flight level. This approach is presented as a computationally scalable means of propagating obstacle dynamics. The reduced order DMD representation of time-varying ice obstacles is integrated with a recently developed backtracking hybrid A∗ graph search algorithm. The backtracking mechanism allows us to determine a feasible path in a computationally scalable manner in the presence of resource constraints. Illustrative numerical results are presented to demonstrate the effectiveness of the proposed path-planning method.