Autonomous Navigation Algorithm for Planetary Rovers Based on Multimodality

Planetary rovers are essential robotic exploration devices that are vital for the investigation of extraterrestrial environments, where they analyze the atmospheric conditions and terrain. These rovers must endure intense acceleration, withstand severe environmental conditions, and maintain their functionality for a prolonged period. These robotic explorers operate in distant and hostile environments where human intervention is not feasible. In the quest to surmount these challenges, they are required to function autonomously and rely on state-of-the-art technologies. This article proposes two multimodal architectures, one which fuses object detection and semantic segmentation, and the other which infuses monocular depth estimation and semantic segmentation, to drive the rover autonomously. The proposed multimodal architectures are robust and enable the rover to navigate autonomously in highly uncertain and unpredictable environments. The results show that implementing the proposed architectures can successfully achieve the rover's autonomous navigation.