Update index.html

Author: BrendanEnglot

index.html

@@ -58,6 +58,23 @@ <h2>Robust Autonomy in Complex Environments</h2>
 <h2>Recent News:</h2>
 <div style="text-align: justify;">
 <br />
+<h2>ICRA 2024 Papers on Autonomous Navigation under Uncertainty 
+</h2><br /> 
+<div style="text-align: center;"><video width="560" height="315" controls> <source src="./Lin_ICRA24_Video.mp4" type=video/mp4> </video><br />
+</div><br /> 
+We recently presented three papers at ICRA 2024 addressing different forms of autonomous navigation under uncertainty. The first paper, "Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning,"
+addresses ASV navigation in congested and disturbance-filled environments. A preprint of the paper is available on <a style="font-weight: bold;" href="https://arxiv.org/abs/2402.11799">arXiv</a>, the corresponding code is available on <a style="font-weight: bold;" href="https://github.com/RobustFieldAutonomyLab/Multi_Robot_Distributional_RL_Navigation">GitHub</a>,
+and more details are illustrated in the accompanying <a style="font-weight: bold;" href="https://robustfieldautonomylab.github.io/Lin_ICRA24_Video.mp4">video attachment</a> (shown above). This research was
+led by Xi Lin.
+The second paper, "Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization,"
+uses virtual maps to support high-performance multi-robot exploration of unknown environments. A preprint of the paper is available on <a style="font-weight: bold;" href="https://arxiv.org/abs/2403.04021">arXiv</a>, the corresponding code is available on <a style="font-weight: bold;" href="https://github.com/RobustFieldAutonomyLab/Multi-Robot-EM-Exploration">GitHub</a>,
+and more details are illustrated in the accompanying <a style="font-weight: bold;" href="https://robustfieldautonomylab.github.io/Huang_ICRA24_Video.mp4">video attachment</a>. This research was
+led by Yewei Huang.
+The third paper, "Real-Time Planning Under Uncertainty for AUVs Using Virtual Maps,"
+also uses virtual maps, as a tool to support planning under localization uncertainty across long distances underwater. A preprint of the paper is available on <a style="font-weight: bold;" href="https://arxiv.org/abs/2403.04936">arXiv</a> 
+and more details are illustrated in the accompanying <a style="font-weight: bold;" href="https://robustfieldautonomylab.github.io/Collado_ICRA24_Video.mp4">video attachment</a>. This research was
+led by Ivana Collado-Gonzalez.
+<h2><br />  
 <h2>New Papers and Code on Distributional Reinforcement Learning</h2>
 <br />
 <div style="text-align: center;"><img style="width: 675px; height: 300px;" alt="" src="./Lin_IROS_2023_CoverImage.png" /><br />