Safe Path Planning for Unmanned Aerial Vehicle under Location Uncertainty
Safe Path Planning for Unmanned Aerial Vehicle under Location Uncertainty
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DOI：
10.1109/icca51439.2020.9264542
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Pengcheng Wu;Junfei Xie;Jun Chen
中科院分区：
文献类型：
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作者：
Pengcheng Wu;Junfei Xie;Jun Chen
在无人机自主飞行过程中，满足避碰要求的路径规划对于无人机在动态不确定环境中的导航至关重要。为此，本文提出了一种基于快速探索随机树（RRT）的概率无碰撞规划方法。该方法利用机会约束，在无人机模型本身或障碍物的位置不确定的情况下，为无人机找到一条满足期望安全要求的轨迹。通过假设高斯分布，机会约束所产生的不确定性，制定通过转换成等效的静态，确定性的约束，即概率界的动态，概率约束。同时，引入了相对不确定度的概念，将不同形式的不确定度转化为一个共同的不确定度。在此基础上，将公式化的机会约束与RRT相结合，提出了一种机会约束RRT规划算法。通过数值试验验证了所提出的路径规划方法在不违反风险约束的前提下的可行性。
For the autonomous operations of unmanned aerial vehicles (UAVs), path planning satisfying collision avoidance plays an essential role in navigating through dynamic and uncertain environments. To this end, a probabilistic collision-free planning method based on Rapidly-exploring Random Tree (RRT) is presented in this paper. This method employs chance constraints to find a trajectory satisfying desired safety requirement for the UAV under location uncertainties for both the UAV model itself or obstacles. By assuming Gaussian distributions, the chance constraints arising from those uncertainties are formulated through converting dynamic, probabilistic constraints into equivalent static, deterministic constraints, namely the probabilistic bound. Also, a method named relative uncertainty is introduced to reduce different forms of uncertainties into a common one. On this basis, a chance constrained RRT planning algorithm is developed through combining formulated chance constraints with RRT. The feasibility of our proposed path planning method without violating the prescribed risk bound is well validated through numerical trials.