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Hazardous and remote environments, such as volcanic regions and disaster zones, require regular monitoring due to the significant risks they pose. Innovative solutions like aerial and mobile robots offer safer monitoring options, though drones are limited by short battery life, and mobile robots face mobility challenges. Collaborative aerial-ground robots have been developed to address these issues, with drones transporting and recovering ground robots. Deploying the ground robot is easy, but its recovery is challenging due to drone Global Positioning System (GPS) inaccuracies. It highlights the need for a precise visual alignment technique. This study utilized YOLOv8n segmentation model to develop a ground robot detection model. The development involved gathering images of ground robots in various environmental settings such as rocks, seashores, sand, grasslands, limestone, and soil. Three models were tested in this study: plate detection, ground robot detection, and a combined plate and ground robot detection model. The ground robot detection model emerged as the most robust, achieving an F1 score of 99.7% and an mAP of 95.4% at 0.5 to 0.96 IoU thresholds. The model was integrated into a small computer on a visual servoing drone and tested outdoors. The alignment threshold was set at 65 cm, based on the size of the retrieval mechanism. The experiments indicate that the drone achieved an average alignment error of 41.985 cm within 7.82 seconds. The experimental results exhibit the effectiveness of the UAV-UGV alignment for the retrieval task in hazardous and remote areas.
Pepito et al. (Wed,) studied this question.