Update papers.bib

Author: jackbergus

_bibliography/papers.bib

@@ -20,7 +20,8 @@ @Article{ai6040071
 URL = {https://www.mdpi.com/2673-2688/6/4/71},
 ISSN = {2673-2688},
 ABSTRACT = {Marine mammal monitoring, a growing field of research, is critical to cetacean conservation. Traditional `tagging’ attaches sensors such as GPS to such animals, though these are intrusive and susceptible to infection and, ultimately, death. A less intrusive approach exploits UUV commanded by a human operator above ground. The development of AI for autonomous underwater vehicle navigation models training environments in simulation, providing visual and physical fidelity suitable for sim-to-real transfer. Previous solutions, including UVMS and L2D, provide only satisfactory results, due to poor environment generalisation while sensors including sonar create environmental disturbances. Though rich in features, image data suffer from high dimensionality, providing a state space too great for many machine learning tasks. Underwater environments, susceptible to image noise, further complicate this issue. We propose SWIM v2.0,type=symbolslist,unit=},
-DOI = {10.3390/ai6040071}
+DOI = {10.3390/ai6040071},
+html = {https://www.mdpi.com/2673-2688/6/4/71}
 }