The Autonomous Safe Landing Area Determination Method and Obstacle Avoidance Strategy
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摘要: 目标星球表面障碍自主检测技术和安全着陆点的选择是实现探测器深空探测安全着陆的关键.针对探测动力下降段自主安全着陆进行仿真研究，提出了一种基于仿真的探测器自主安全着陆仿真算法.该算法首先确定探测器所携带的电荷耦合器件相机拍摄的表面区域，利用基于光线跟踪算法以及高程数据对月球遥感影像进行仿真.然后基于边缘检测获取探测器目标表面的岩石及陨石坑等障碍，并对这些障碍进行椭圆拟合从而确定障碍区域.在确定障碍区的基础上，利用图像的形态学操作，采用高斯图像金字塔获取各层表面遥感影像，对多帧图像进行实时处理，精确地确定安全着陆区.在着陆区域基本确定之后的精避障阶段，提出了一种基于三维重构的探测器精避障螺旋搜索策略.该算法在传统图像匹配识别的基础上，首先根据探测器所携带的激光三维成像仪对初选着陆区域进行高精度三维重构，然后对重构后的区域进行螺旋搜索，从而选定更精确、更安全的软着陆目标着陆点.最后通过仿真实验验证了算法的有效性.Abstract: The autonomous detection technology of lunar surface obstacles and the selection of safe landing sites are the key to realize the safe landing of the probe on the lunar surface. The autonomous safe landing of lunar exploration power descent phase is simulated, and a simulation algorithm for autonomous safe landing of spacecraft based on simulation is proposed. Firstly, the lunar region captured by the CCD camera carried by the detector is determined, and the lunar remote sensing image is simulated based on ray tracing algorithm and elevation data. Then, based on edge detection, obstacles such as rocks and craters on the target surface of the detector are obtained, and these obstacles are fitted elliptically to determine the obstacle area. On the basis of determining obstacle areas, using morphological operation of images, the remote sensing images of each layer of the moon are acquired by using the Gauss image pyramid, and the multi-frame images are processed in real time to determine the safe landing area accurately. In the stage of obstacle avoidance after the landing area is basically determined, a spiral search strategy for obstacle avoidance of detector based on three-dimensional reconstruction is proposed. On the basis of traditional image matching recognition, the algorithm firstly reconstructs the lunar landing area with high precision according to the laser three-dimensional imager carried by the detector, and then searches the reconstructed area spirally to select a more precise and safer landing site for the soft landing target. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.
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