Alapfogalmak
A novel system that integrates Extended Reality (XR) and Artificial Intelligence (AI) to enable immersive teleoperation of lunar rovers, with autonomous rock detection and 3D visualization of the environment, improving operator decision-making and exploration effectiveness.
Kivonat
The presented work proposes a novel system that combines Extended Reality (XR) and Artificial Intelligence (AI) to enhance the teleoperation of lunar rovers. The system consists of three main subsystems: the lunar rover, the ROS PC, and the XR PC.
The first phase involves collecting sensory data from the rover's sensors, including RGB-D information, color images, and depth data. In the second phase, this data is processed using the YOLOv5 CNN algorithm to detect rocks in 2D images, and a 3D mesh of the rover's surroundings is generated based on RTAB-MAP and 3D point cloud environment generation.
The third phase visualizes the processed data in a 3D reconstructed XR environment, providing the operator with a dynamic view of the rover's surroundings, including a 3D model of the rover and marked 3D visual indicators for identified rocks and their positions. This comprehensive approach enhances the operator's ability to discern the positions of rocks proximate to the rover in a lunar environment.
The system was validated through experiments conducted in an analogue lunar laboratory, the LunaLab, at the University of Luxembourg. The findings from the experiments demonstrate the significant impact of the XR system in minimizing the cognitive load of operators and improving their perception of the environment compared to traditional 2D-based teleoperation approaches.
The authors highlight the importance of expanding the range and functionality of the XR system to include enhanced ranging and reporting capabilities in the virtual lunar environment, which will increase the rover's safety when navigating challenging terrain and provide operators with critical data to make informed operational adjustments and optimize exploration routes.
Statisztikák
The system utilizes the YOLOv5 CNN algorithm for rock detection in 2D images and the RTAB-MAP algorithm for generating a 3D mesh of the rover's surroundings.
Idézetek
"The implementation of the XR system has not only shown a significant impact in minimizing the cognitive load of the operators in complex areas with obstacles, but also, participants reported a greater perception of the environment while using the XR system."
"Accurate integration of distance metrics and real-time alerts within the XR system will not only increase the rover's safety when navigating challenging terrain, but also provide operators with critical data to make informed operational adjustments and optimize exploration routes."