A novel learning-based global path planning algorithm for planetary rovers

Autonomous path planning algorithm is significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions. In this paper, a novel learning-based algorithm is proposed to deal with global path planning problem for planetary exploration rovers. Specifically, a novel deep convolutional neural network with dual branches (DB-CNN) is designed and trained, which 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 compared with the existing Value Iteration Network (VIN).