城市风场环境中的无人机快速航迹规划方法
收稿日期: 2015-04-29
修回日期: 2015-08-26
网络出版日期: 2015-08-29
基金资助
国家自然科学基金(61473233)
A rapid trajectory planning algorithm for UAV in urban areas with wind fields
Received date: 2015-04-29
Revised date: 2015-08-26
Online published: 2015-08-29
Supported by
National Natural Science Foundation of China(61473233)
密集的城市障碍环境以及复杂的城市风场干扰对航迹规划的实时性和航迹跟踪的准确性提出了严格要求,为此提出一种城市风场环境中的小型无人机(UAVs)快速航迹规划方法。首先,为了保证航迹规划的高效性,对固定翼无人机运动学方程进行了合理简化。其次,由于障碍环境中的最优航迹难以直接完全塑造,因此根据状态受限的最优控制理论给出了可以使用螺旋线与直线构建近似最优航迹的结论,并据此提出了一种针对城市环境的三维航迹规划方法。然后,通过对无人机运动学模型的分析,从规划角度提出了风场干扰下的航迹设计准则。仿真实验中,首先通过算法对比实验,验证了航迹规划方法的高效性;然后使用六自由度(DOF)飞机模型分别在无风场干扰和有风场干扰的环境下进行了航迹跟踪实验,实验结果证明了风场干扰下航迹设计准则的有效性。
李俨 , 王重 , 齐延军 , 王怡馨 . 城市风场环境中的无人机快速航迹规划方法[J]. 航空学报, 2016 , 37(3) : 949 -959 . DOI: 10.7527/S1000-6893.2015.0236
A rapid trajectory planning algorithm for small unmanned aerial vehicles(UAVs) in urban areas with wind fields is proposed. The dense obstacles and complex wind fields in the urban area have posed strict requirements on the real-time capability and tracking performance of trajectory planning. Firstly, the kinematic model of the fixed-wing UAV is reasonably simplified to ensure the efficiency of the trajectory planning algorithm. Based on the basic principles of the constrained optimal control theory, the optimal trajectory in the presence of obstacles cannot be directly and fully characterized. And it is shown that the optimal trajectory can be reasonably approximated using spiral and straight line segments. Then a rapid three-dimensional near-optimal trajectory planning algorithm in urban areas is proposed. From a perspective of planning, the new trajectory planning standards and constraints in wind fields are presented. The comparison simulations show the efficiency of the proposed algorithm. Trajectory tracking performance simulations with and without wind fields are also implemented using a 6 degree-of-freedom(DOF) UAV model. And the numerical results also show the effectiveness of the proposed planning standards in wind fields.
Key words： unmanned aerial vehicle; fast algorithms; optimal control; urban area; wind effect
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