Belongs to “Research on Navigation and Guidance Control of Precise Landing on Planetary Surface”, 973 National Basic Research Program of China, lead by Prof. Yuanqing Xia.
Autonomous path planning algorithms are significant to planetary exploration rovers, due that relying on commands from Earth will heavily reduce their efficiency of executing exploration missions. We proposes a novel learning-based architecture to address global path planning problem for planetary exploration rovers. Specifically, a novel deep convolutional neural network with double branches (DB-CNN) is designed and trained. It can plan path directly from orbital images of planetary surfaces without implementing environment mapping. Moreover, the planning procedure requires no prior knowledge about planetary surface terrains. Finally, experimental results demonstrate that DB-CNN achieves better performance on global path planning and faster convergence during training than existing ones.
Jiang Zhang, Yuanqing Xia, Ganghui Shen. A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers. Neurocomputing 361, 69-76.