A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers
This addresses the problem of inefficient autonomous exploration for planetary rovers, though it appears incremental as it builds on existing learning-based methods.
The paper tackles global path planning for planetary rovers by proposing a learning-based algorithm that uses a deep convolutional neural network with double branches (DB-CNN) to plan paths directly from orbital images, achieving better performance and faster convergence compared to the Value Iteration Network.
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).