1 research outputs found
A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers
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).Comment: Submitted to Neurocomputing. arXiv admin note: text overlap with
arXiv:1808.0839