# A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers

arXiv: https://arxiv.org/abs/1811.10437 · HTML: https://mlsanity.com/paper/1811.10437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Value Iteration Networks with Gated Summarization Module](https://mlsanity.com/paper/2305.07039/) (2023)

## Abstract (arXiv)

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).