ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (12): 2649-2659.doi: 10.7544/issn1000-1239.2019.20190218

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The Autonomous Safe Landing Area Determination Method and Obstacle Avoidance Strategy

Jin Tao1, Zhang Dengyi1, Cai Bo2   

  1. 1(School of Computer, Wuhan University, Wuhan 430072);2(School of Cyber Science and Engineering, Wuhan University, Wuhan 430072)
  • Online:2019-12-01

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.

Key words: remote sensing images, ellipse fitting, hazard detection, soft landing, precise obstacle avoidance

CLC Number: