Underwater Robotic Swarms: Lightweight Visual Tracking and Relative Localization Approach

Le résumé fourni par la source

Reliable visual tracking and localization remain significant challenges in underwater swarm robotics due to limited visibility, frequent occlusions, and stringent computational constraints. This technical communication presents a lightweight visual perception framework designed and validated for deployment on the edge of underwater robotic swarms, using the heterogeneous swarm of underwater robotic fish as the experimental platform. Rather than proposing new algorithms, the work focuses on integrating and adapting existing monocular vision-based keypoint detection and multiobject tracking methods for real-time operation on resource-constrained hardware. The framework achieves a balance between accuracy and computational efficiency, demonstrating practical feasibility for edge deployment. Experimental validation showed an average heading error of 17.2$^{\circ }$and an average depth error of 24.4 cm, confirming its robustness in relative localization and orientation estimation under realistic underwater conditions. The ByteTrack–YOLOv5n.ONNX combination achieved 2.01 fps on a Raspberry Pi 4, representing a 70.3% improvement over the PyTorch baseline, while ByteTrack–YOLOv8n achieved the highest tracking metrics with higher order tracking accuracy (HOTA) at 50.4%, multi-object tracking accuracy (MOTA) at 77.9%, and identity F1 score (IDF1) at 63.6%. Overall, this work contributes a validated reference for efficient visual perception and localization in underwater swarm robotics, supporting future research on scalable, energy-aware robotic networks for marine applications.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.