2018/11/23 by Jiang Zhang, Yuanqing Xia, Zhang, Jiang +3
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Guidance and Control Systems #Robotic Path Planning Algorithms #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.1811.10437
openalex publication_date 2018/11/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Autonomous path planning algorithms are significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions. This paper proposes a novel learning-based algorithm to deal with global path planning problem for planetary exploration rovers. Specifically, a novel deep convolutional neural network with double 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).